{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lb5yiH5h8x3h"
      },
      "source": [
        "##### Copyright 2025 Google LLC."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "906e07f6e562"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YXR7Yn480fU"
      },
      "source": [
        "# 2D spatial understanding with Gemini"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CeJyB7rG82ph"
      },
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/quickstarts/Spatial_understanding.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" height=30/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "___eV40o8399"
      },
      "source": [
        "This notebook introduces object detection and spatial understanding with the Gemini API like in the [Spatial understanding example](https://aistudio.google.com/starter-apps/spatial) from [AI Studio](https://aistudio.google.com) and demonstrated in the [Building with Gemini 2.0: Spatial understanding](https://www.youtube.com/watch?v=-XmoDzDMqj4) video.\n",
        "\n",
        "You'll learn how to use Gemini the same way as in the demo and perform object detection like this:\n",
        "<img src=\"https://storage.googleapis.com/generativeai-downloads/images/cupcakes_with_bbox.png\" />\n",
        "\n",
        "There are many examples, including object detection with\n",
        "\n",
        "* simply overlaying information\n",
        "* searching within an image\n",
        "* translating and understanding things in multiple languages\n",
        "* using Gemini thinking abilities\n",
        "\n",
        "**Note**\n",
        "\n",
        "There's no \"magical prompt\". Feel free to experiment with different ones. You can use the dropdown to see different samples, but you can also write your own prompts. Also, you can try uploading your own images.\n",
        "\n",
        "----"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4dbK2FbXCJE"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7o6sel20XCJP"
      },
      "source": [
        "### Install SDK"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u53CdzCFXCJP"
      },
      "outputs": [],
      "source": [
        "%pip install -U -q \"google-genai>=1.16.0\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsNLA8_YXCJQ"
      },
      "source": [
        "### Setup your API key\n",
        "\n",
        "To run the following cell, your API key must be stored in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see [Authentication ![image](https://storage.googleapis.com/generativeai-downloads/images/colab_icon16.png)](../quickstarts/Authentication.ipynb) for an example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TaPzmJ07XCJQ"
      },
      "outputs": [],
      "source": [
        "from google.colab import userdata\n",
        "import os\n",
        "\n",
        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Hx_Gw9i0Yuv"
      },
      "source": [
        "### Initialize SDK client\n",
        "\n",
        "With the new SDK you now only need to initialize a client with your API key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HghvVpbU0Uap"
      },
      "outputs": [],
      "source": [
        "from google import genai\n",
        "from google.genai import types\n",
        "\n",
        "client = genai.Client(api_key=GOOGLE_API_KEY)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6eW_IDAkXCJQ"
      },
      "source": [
        "### Select and configure a model\n",
        "\n",
        "Spatial understanding works best [Gemini 2.0 Flash model](https://ai.google.dev/gemini-api/docs/models/gemini-v2). It's even better with 2.5 models like `gemini-2.5-pro` but slightly slower as it's a [thinking](./Get_started_thinking.ipynb) model.\n",
        "\n",
        "Some features, like segmentation, only works with 2.5 models.\n",
        "\n",
        "The [Object detection](https://github.com/google-gemini/cookbook/blob/gemini-1.5-archive/examples/Object_detection.ipynb) contains good examples of what previous models were able to do.\n",
        "\n",
        "For more information about all Gemini models, check the [documentation](https://ai.google.dev/gemini-api/docs/models/gemini) for extended information on each of them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ydWzoN9nXCJQ"
      },
      "outputs": [],
      "source": [
        "model_name = \"gemini-2.0-flash\" # @param [\"gemini-2.0-flash\", \"gemini-2.5-flash-lite\", \"gemini-2.5-flash\", \"gemini-2.5-pro\",\"gemini-3-pro-preview\"] {\"allow-input\":true}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7RuQjPJ07Klh"
      },
      "source": [
        "### System instructions\n",
        "\n",
        " With the new SDK, the `system_instructions` and the `model` parameters must be passed in all `generate_content` calls, so let's save them to not have to type them all the time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lz1yU91P7IfN"
      },
      "outputs": [],
      "source": [
        "bounding_box_system_instructions = \"\"\"\n",
        "    Return bounding boxes as a JSON array with labels. Never return masks or code fencing. Limit to 25 objects.\n",
        "    If an object is present multiple times, name them according to their unique characteristic (colors, size, position, unique characteristics, etc..).\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LtEryXuFDwRH"
      },
      "outputs": [],
      "source": [
        "safety_settings = [\n",
        "    types.SafetySetting(\n",
        "        category=\"HARM_CATEGORY_DANGEROUS_CONTENT\",\n",
        "        threshold=\"BLOCK_ONLY_HIGH\",\n",
        "    ),\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufl4g-cUuR9E"
      },
      "source": [
        "The system instructions are mainly used to make the prompts shorter by not having to reapeat each time the format. They are also telling the model how to deal with similar objects which is a nice way to let it be creative.\n",
        "\n",
        "The [Spatial understanding example](https://aistudio.google.com/starter-apps/spatial) is using a different strategy with no system instructions but a longer prompt. You can see their full prompts by clicking on the \"show raw prompt\" button on the right. There no optimal solution, experiment with diffrent strategies and find the one that suits your use-case the best.\n",
        "\n",
        "It is also recommend to always disable the [thinking](./Get_started_thinking.ipynb), as so far it adds latency without improving the results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GOOZsm7i9io6"
      },
      "source": [
        "### Import\n",
        "\n",
        "Import all the necessary modules."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8aBLGCq09GIe"
      },
      "outputs": [],
      "source": [
        "import google.generativeai as genai\n",
        "from PIL import Image\n",
        "\n",
        "import io\n",
        "import os\n",
        "import requests\n",
        "from io import BytesIO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n4Vx-ZbZCqWf"
      },
      "source": [
        "### Utils\n",
        "\n",
        "Some scripts will be needed to draw the bounding boxes. Of course they are just examples and you are free to just write your own.\n",
        "\n",
        "For example the [Spatial understanding example](https://aistudio.google.com/starter-apps/spatial) from [AI Studio](https://aistudio.google.com) uses HML to render the bounding boxes. You can find its code in the [Github repo](https://github.com/google-gemini/starter-applets/tree/main/spatial)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "cHbhdPsH1PAS"
      },
      "outputs": [],
      "source": [
        "# @title Parsing JSON output\n",
        "def parse_json(json_output: str):\n",
        "    # Parsing out the markdown fencing\n",
        "    lines = json_output.splitlines()\n",
        "    for i, line in enumerate(lines):\n",
        "        if line == \"```json\":\n",
        "            json_output = \"\\n\".join(lines[i+1:])  # Remove everything before \"```json\"\n",
        "            json_output = json_output.split(\"```\")[0]  # Remove everything after the closing \"```\"\n",
        "            break  # Exit the loop once \"```json\" is found\n",
        "    return json_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "oxK0pycZm4AY"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "Suggested packages:\n",
            "  fonts-noto-cjk-extra\n",
            "The following NEW packages will be installed:\n",
            "  fonts-noto-cjk\n",
            "0 upgraded, 1 newly installed, 0 to remove and 35 not upgraded.\n",
            "Need to get 61.2 MB of archives.\n",
            "After this operation, 93.2 MB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu jammy/main amd64 fonts-noto-cjk all 1:20220127+repack1-1 [61.2 MB]\n",
            "Fetched 61.2 MB in 3s (17.9 MB/s)\n",
            "Selecting previously unselected package fonts-noto-cjk.\n",
            "(Reading database ... 126319 files and directories currently installed.)\n",
            "Preparing to unpack .../fonts-noto-cjk_1%3a20220127+repack1-1_all.deb ...\n",
            "Unpacking fonts-noto-cjk (1:20220127+repack1-1) ...\n",
            "Setting up fonts-noto-cjk (1:20220127+repack1-1) ...\n",
            "Processing triggers for fontconfig (2.13.1-4.2ubuntu5) ...\n"
          ]
        }
      ],
      "source": [
        "# @title Plotting Util\n",
        "\n",
        "# Get Noto JP font to display janapese characters\n",
        "!apt-get install fonts-noto-cjk  # For Noto Sans CJK JP\n",
        "\n",
        "#!apt-get install fonts-source-han-sans-jp # For Source Han Sans (Japanese)\n",
        "\n",
        "import json\n",
        "import random\n",
        "import io\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "from PIL import ImageColor\n",
        "\n",
        "additional_colors = [colorname for (colorname, colorcode) in ImageColor.colormap.items()]\n",
        "\n",
        "def plot_bounding_boxes(im, bounding_boxes):\n",
        "    \"\"\"\n",
        "    Plots bounding boxes on an image with markers for each a name, using PIL, normalized coordinates, and different colors.\n",
        "\n",
        "    Args:\n",
        "        img_path: The path to the image file.\n",
        "        bounding_boxes: A list of bounding boxes containing the name of the object\n",
        "         and their positions in normalized [y1 x1 y2 x2] format.\n",
        "    \"\"\"\n",
        "\n",
        "    # Load the image\n",
        "    img = im\n",
        "    width, height = img.size\n",
        "    print(img.size)\n",
        "    # Create a drawing object\n",
        "    draw = ImageDraw.Draw(img)\n",
        "\n",
        "    # Define a list of colors\n",
        "    colors = [\n",
        "    'red',\n",
        "    'green',\n",
        "    'blue',\n",
        "    'yellow',\n",
        "    'orange',\n",
        "    'pink',\n",
        "    'purple',\n",
        "    'brown',\n",
        "    'gray',\n",
        "    'beige',\n",
        "    'turquoise',\n",
        "    'cyan',\n",
        "    'magenta',\n",
        "    'lime',\n",
        "    'navy',\n",
        "    'maroon',\n",
        "    'teal',\n",
        "    'olive',\n",
        "    'coral',\n",
        "    'lavender',\n",
        "    'violet',\n",
        "    'gold',\n",
        "    'silver',\n",
        "    ] + additional_colors\n",
        "\n",
        "    # Parsing out the markdown fencing\n",
        "    bounding_boxes = parse_json(bounding_boxes)\n",
        "\n",
        "    font = ImageFont.truetype(\"NotoSansCJK-Regular.ttc\", size=14)\n",
        "\n",
        "    # Iterate over the bounding boxes\n",
        "    for i, bounding_box in enumerate(json.loads(bounding_boxes)):\n",
        "      # Select a color from the list\n",
        "      color = colors[i % len(colors)]\n",
        "\n",
        "      # Convert normalized coordinates to absolute coordinates\n",
        "      abs_y1 = int(bounding_box[\"box_2d\"][0]/1000 * height)\n",
        "      abs_x1 = int(bounding_box[\"box_2d\"][1]/1000 * width)\n",
        "      abs_y2 = int(bounding_box[\"box_2d\"][2]/1000 * height)\n",
        "      abs_x2 = int(bounding_box[\"box_2d\"][3]/1000 * width)\n",
        "\n",
        "      if abs_x1 > abs_x2:\n",
        "        abs_x1, abs_x2 = abs_x2, abs_x1\n",
        "\n",
        "      if abs_y1 > abs_y2:\n",
        "        abs_y1, abs_y2 = abs_y2, abs_y1\n",
        "\n",
        "      # Draw the bounding box\n",
        "      draw.rectangle(\n",
        "          ((abs_x1, abs_y1), (abs_x2, abs_y2)), outline=color, width=4\n",
        "      )\n",
        "\n",
        "      # Draw the text\n",
        "      if \"label\" in bounding_box:\n",
        "        draw.text((abs_x1 + 8, abs_y1 + 6), bounding_box[\"label\"], fill=color, font=font)\n",
        "\n",
        "    # Display the image\n",
        "    img.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqPO819ysM3c"
      },
      "source": [
        "### Get example images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Pb2qWTIqsImv"
      },
      "outputs": [],
      "source": [
        "# Load sample images\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/socks.jpg -O Socks.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/vegetables.jpg -O Vegetables.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/Japanese_Bento.png -O Japanese_bento.png -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/Cupcakes.jpg -O Cupcakes.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/origamis.jpg -O Origamis.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/fruits.jpg -O Fruits.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/cat.jpg -O Cat.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/pumpkins.jpg -O Pumpkins.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/breakfast.jpg -O Breakfast.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/bookshelf.jpg -O Bookshelf.jpg -q\n",
        "!wget https://storage.googleapis.com/generativeai-downloads/images/spill.jpg -O Spill.jpg -q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WFLDgSztv77H"
      },
      "source": [
        "## Overlaying Information\n",
        "\n",
        "Let's start by loading an image, the origami one for example:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eSFBHGJjxIJb"
      },
      "outputs": [
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=620x620>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = \"Cupcakes.jpg\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "\n",
        "im = Image.open(image)\n",
        "im.thumbnail([620,620], Image.Resampling.LANCZOS)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wYfXLLgRyDfo"
      },
      "source": [
        "Let's start with a simple prompt to find all items in the image.\n",
        "\n",
        "To prevent the model from repeating itself, it is recommended to use a temperature over 0, in this case 0.5. Limiting the number of items (25 in the systemp instructions) is also a way to prevent the model from looping and to speed up the decoding of the bounding boxes. You can experiment with these parameters and find what works best for your use-case.\n",
        "\n",
        "It is also recommend to always disable the [thinking](./Get_started_thinking.ipynb), as so far it adds latency without improving the results."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "phxOEE6Gx_Vy"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "```json\n",
            "[\n",
            "  {\"box_2d\": [390, 64, 574, 203], \"label\": \"red sprinkles\"},\n",
            "  {\"box_2d\": [382, 250, 537, 369], \"label\": \"pink and blue sprinkles\"},\n",
            "  {\"box_2d\": [365, 397, 501, 509], \"label\": \"pink frosting\"},\n",
            "  {\"box_2d\": [355, 529, 521, 650], \"label\": \"pink frosting with blue balls\"},\n",
            "  {\"box_2d\": [384, 737, 535, 866], \"label\": \"chocolate frosting\"},\n",
            "  {\"box_2d\": [443, 432, 595, 564], \"label\": \"pink frosting with googly eyes\"},\n",
            "  {\"box_2d\": [477, 627, 638, 770], \"label\": \"white frosting with colorful sprinkles\"},\n",
            "  {\"box_2d\": [556, 40, 726, 200], \"label\": \"white frosting with colorful sprinkles\"},\n",
            "  {\"box_2d\": [510, 799, 690, 959], \"label\": \"white frosting with colorful candies\"},\n",
            "  {\"box_2d\": [545, 295, 702, 444], \"label\": \"white frosting with googly eyes\"},\n",
            "  {\"box_2d\": [559, 514, 712, 663], \"label\": \"white frosting with googly eyes\"},\n",
            "  {\"box_2d\": [429, 597, 638, 770], \"label\": \"white frosting with colorful sprinkles\"},\n",
            "  {\"box_2d\": [713, 271, 874, 497], \"label\": \"white frosting with googly eyes\"},\n",
            "  {\"box_2d\": [655, 350, 816, 516], \"label\": \"white frosting with googly eyes\"},\n",
            "  {\"box_2d\": [743, 133, 921, 307], \"label\": \"white frosting with googly eyes\"}\n",
            "]\n",
            "```\n"
          ]
        }
      ],
      "source": [
        "prompt = \"Detect the 2d bounding boxes of the cupcakes (with “label” as topping description”)\"  # @param {type:\"string\"}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(BytesIO(open(image, \"rb\").read()))\n",
        "im.thumbnail([1024,1024], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find bounding boxes\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        system_instruction=bounding_box_system_instructions,\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Check output\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wxlObJhF00mR"
      },
      "source": [
        "As you can see, even without any instructions about the format, Gemini is trained to always use this format with a label and the coordinates of the bounding box in a \"box_2d\" array.\n",
        "\n",
        "Just be careful, the y coordinates are first, x ones afterwards contrary to common usage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZBNvc1XzJP5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Output hidden; open in https://colab.research.google.com to view."
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_bounding_boxes(im, response.text)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjP8ktS62QRv"
      },
      "source": [
        "## Search within an image\n",
        "\n",
        "Let's complicate things and search within the image for specific objects."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jlF_8H2f2ZEI"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "```json\n",
            "[\n",
            "  {\"box_2d\": [46, 246, 385, 526], \"label\": \"light blue sock with cat face on left\"},\n",
            "  {\"box_2d\": [233, 661, 650, 862], \"label\": \"light blue and grey sock with cat face on right\"}\n",
            "]\n",
            "```\n",
            "(640, 482)\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x482>"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = \"Socks.jpg\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "prompt = \"Show me the positions of the socks with the face\"  # @param [\"Detect all rainbow socks\", \"Find all socks and label them with emojis \", \"Show me the positions of the socks with the face\",\"Find the sock that goes with the one at the top\"] {\"allow-input\":true}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(image)\n",
        "im.thumbnail([640,640], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find bounding boxes\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        system_instruction=bounding_box_system_instructions,\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Check output\n",
        "print(response.text)\n",
        "\n",
        "# Generate image with bounding boxes\n",
        "plot_bounding_boxes(im, response.text)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SctVyhDFMOK"
      },
      "source": [
        "Try it with different images and prompts. Different samples are proposed but you can also write your own."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvVSSr7z3uN4"
      },
      "source": [
        "## Multilinguality\n",
        "\n",
        "As Gemini is able to understand multiple languages, you can combine spatial reasoning with multilingual capabilities.\n",
        "\n",
        "You can give it an image like this and prompt it to label each item with Japanese characters and English translation. The model reads the text and recognize the pictures from the image itself and translates them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NLmbPUTg3wxx"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(640, 640)\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=640x640>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = \"Japanese_bento.png\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "prompt = \"Detect food, label them with Japanese characters + english translation.\"  # @param [\"Detect food, label them with Japanese characters + english translation.\", \"Show me the vegan dishes\",\"Explain what those dishes are with a 5 words description\",\"Find the dishes with allergens and label them accordingly\"] {\"allow-input\":true}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(image)\n",
        "im.thumbnail([640,640], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find bounding boxes\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        system_instruction=bounding_box_system_instructions,\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Generate image with bounding boxes\n",
        "plot_bounding_boxes(im, response.text)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZbhjYkUA86w"
      },
      "source": [
        "## Use Gemini reasoning capabilities\n",
        "\n",
        "The model can also reason based on the image, you can ask it about the positions of items, their utility, or, like in this example, to find the shadow of a speficic item."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_-IHhnxgBNdD"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(640, 482)\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x482>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = \"Origamis.jpg\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "prompt = \"Draw a square around the fox' shadow\"  # @param [\"Find the two origami animals.\", \"Where are the origamis' shadows?\",\"Draw a square around the fox' shadow\"] {\"allow-input\":true}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(image)\n",
        "im.thumbnail([640,640], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find bounding boxes\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        system_instruction=bounding_box_system_instructions,\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Generate image with bounding boxes\n",
        "plot_bounding_boxes(im, response.text)\n",
        "im"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Un65SR4JRv2s"
      },
      "source": [
        "You can also use Gemini knowledge to enhanced the labels returned. In this example Gemini will give you advice on how to fix your little mistake.\n",
        "\n",
        "As you can see this time, you're only resizing the image to 1024px as it helps the model getting the bigger picture and give you advice. There's no clear rule about when to do it, experiment and find what works the best for you."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gz6_AcMNRvEm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(640, 480)\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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wtr6L5zDJE9xMAG253DecA43YHrjipAYLlb+COaG5gFpYIWS6W3DOseOJHGB0PykZPttrmJlDMFPINJBe/Y4p7ZrWC4tpmV2im3YDKCAwKkEH5j371nVwMrXjK70/Dlu+n8vdeocx0DW8suoapdagYYbyVo5Y7ZrsJFIhz8+8H58cdDk5Jq7e6pbpL4gkt7qLdObfa0b58wY+bGeT7/rXK3N7Le3CzSBF2oI0RFwqKBgACq7gnFOGAu1Ko9ktFsrOL/8AbfxDmOk066il0u+sknhgu5GjKyPJ5YmRd2QWPAOSpweDimpvm0j7FBqVvFdQ3jSyu9ztEgKqFYOfvbSG6c85Fc4AeBTnGF5XINbzwnNJyT3d9utrfdb8RJnQ3eqQSL4kltLkJ9plh2ENsMoBO4gdcHr+NTzzw31veW6XluJ59NtMPJMqhnQJuBYnG7joT2rl1iZiCOnQUnkO7HaKz/s6Ctyva34ctv8A0lD5jp7b7HbaZcILu2kWfS2XzJbol/M2g+WI8/KAwwMjnHB5xUSajBFIzQ3iKw0JYldZAD5nHy9fve3WsD7BLIDhelEenEqSx5Pal/Z8W25SvcOY6XTb62jXQbm6njZo7WeFmdifLZmkCbsHcMbgfUDkVLDqT2qTQMbC3EdnOYWtLkyHc5TI3F25+XIAOeprn4raYAAKGUeo61eisTtb5FAIx9KJZbCUrt/1dv8A9ua9A5yvoU8cXiK3mllVECybnkYAAlGHU+9alkI7jw6sd5NAq28En2eSOcCQEkny2j/iBJ644z17VkSWyK+DgEU9INqkhtw6VtWwiqT572en4X/zEpGtpPlzaeLS8mt47IStJ5guPLmhbaBuAP3wcDgA9O1XtGu0XTdLkt0sfMtFfzBcTMjK25juC7wGBBA6E8Y6Yrl3jBGOnY1Zt49kDnkALWVTAxqNtvd3t02afXrfy/O75j0kG3X4WuROJIzaMd7no5J+Xn0Y7R9BXJ297aiWMeZbM02lJAhkkIVXBGVYqQVyAR1HUdjWuOfgx/n/AJ+K89WvDyrBqr9YvJ6VJL8jWcrW9Dqo7wvcT20kllaz/YPs9vJDNuRfnDYLlm5I3DOccgUy9cCx0uA3Mc8sUTrIY5N+07yQM/QiubFW7Q/NXtU8GoTUk9tfnZr1tr95m5HZxTR/aILz7VGLRLURNBv+bOzaU29Tluc9Oc1l+IZfNtbCFZdyLDGCobgEDn8agjXJFQ6j9+NfQE0qeCjTmp3vbRfj+Ou4c1zV0q5WOTQEF0AkMjGVfM4XL9W9OPXtWhaXcS26SXUgcJerKwZsnbjk/TiuY0+PJZvU1F4hvhbWYtEP72cfN7J/9eplgKdm29/82/1Gm27Fu41VrSS6uZNQgupHu4prRIphJjYSS5x93IOMHBOenFNuLnT9M1XTYoXSexW7N7LsIbAZhtU+6oo49Sa5OOPCg/rVhFPQ8+lcrwUW7t779Olvw/M6Udzbaj9mvFSV9OSGWR2W4hnaRg5R1Vzl2KjLDOQD09KxfE8RbwpDAbqGa5junl2xyh9oKgdvXBrJjywxxVhU4wRnPHJqaeDUJqd9rfhf/Mo3fCV1A/hnSJ5HQi1uH86MsN2C4PCnkj6elLeMYdJu0uLyK7kuJ1aLy5N/ILZc/wB3g4weefasHwwVW1kgJ4SV1H58VNMQisv/AE0yB7Vbwi57363/AB5vz/AlGxocUr+HZIobiOH/AE4k+ZLsB+Qdz6VPrOoxtbay9vcYaQwqrA7TJg4Yjvz1/GsKC9lVDZAL5fmmXPfJGPy4pmot+6bI6FR/KpWE5puU+/6p/ogPL9+V6nNRld3Pf0pcOeAv50wxv1L/AJV7Rzg0RQ5P86j3Bff1Aqwqxt95sn/aNNcouMc0AQ5YnKCpF3MBufbSL5hOVXHtUvkZOWJz7U2AwCOM/MdynuatR3CZ+RCRjBxjkelQtEg6KPc0+GM84+7nBPvSAkkiLqrIwCHoP7vtUDwls5B3jrmplkaM4IyvdexoJDsNhw/YHvQBAFyNpxmkKLkYH507JVjlfYigKxBIH44oAaRjjFAG7t1p4CBN7Et9KjeRnIUAKPagBQqA4PJPp2pzKEBbG3NIrBcKBlsdhTJg3BZuSOnpQBEWy3TilOOxqMnHQU4UwHDpQ3C5NMaRV4qCSVmHoKLXE2OMu0hgeQcivWtMul1Xw5DLnJ2YP1FeO13/AMPr/fBcWLH7p3KPassRD3b9iqUvesc34jtjb6iWxw4/UVj5rtfGdl+7aQDlDmuIrSjLmgiJq0h1GabSitSQoHWijFAB1oxTgKCKQwUVYhX5hVdTg1cg+7mkxktXrY/KKp1MkgSPOec0kJ7GzZTeTdRvnowruywaSGQdwDXmMc5JGDXoWnT+fpttJ3AwaGSjolXIrrNbQN4T05T0/dD/AMhmuYgG6NT7V1msLnwxYD/rn/6Aa8PNf95wv+L9DSGzPCdMiNtqN7Zkf6uVgPwJrfCArkdKo6jALXxledAsu1/zH/1q0PNiRGywGK94g0Y4wLVcdaFQAg+9VDqtvFboQdxx0FZc2r3MjmONAo7E0BY6IqhCqSADVW+VhBGA5ZBwO3SsbNyY1lMx3A/dqwNQZo1DKSB1oESRwbny3PPNXltwxyFwD2rPiv2Zzsh49TVj7Xcn7uxRQMsy23yHAqKW3HUkA1Re4u2zmXA9qrOzt9+Vj+NADrhfLkzuFU5FDE461MSpPQk+1PihkckJEx9M0xDFXBFSGMEA5FTppty2MgJVmPSCcl5Cc9hxTsBnMqqeTg04IJFAAJxWyul26dVDH35qYRxxjAAosK5kx2rYH7s/jU8VuwJDACtNFyOENPELHqAKLBcorGqnABJPBqcRRqnEYBqyIcdBSiMd6YiqBwRt49RQEfqDVoR9eOKekII4zQBjyQl5jnrU6W6iLP5VajtCZmIBI61ObRggABP4UDMiaECPcByKJDs02dh6BQa0ms5CB8hArM1MeRp4jPVpKljOvX/ki4/z/wAvFeerXoa4PwYH+f8Al4rz8AZArwck/wCYj/r7P9DWp09BQKtW3DioTjNSwnDCvaMzoLZMgGqV+N103faAKv2PzR5rPmJkmcjqW4pMESRTRWGnG4m+6BwO7HsBXKSyS3l09xMcu5yR6D0q9qdx9snEYz5MXyoB39T+NQxRJjuDXPUnfQ6KcLasYifN6VMindnpUoiIGTgj1HapUiJ479jWLZqNRMMCBwasheMdMURxknBHHrU2zGaBmJpVz9k1O6t+QS5dc9+9aV2wKGUDggGqOrWflyR6gv34SAV/vKTyKssxeMQsp2spCEdDVPXUnYEbN4/+ywqfUsGJv98c496pQSfvmZzgt0z6jtV25Bktiw/vZ5+tDQHmEfmOcFiKcYASdzE/jTAwI5wD9aRZQDuAJ7Guw5xBEIzlR1PpUvynjbgionlLblxgDrS7io9KAHkbcEDJ/nSh91R7icjP6UnB6saLBcmVow2JclO+OtPSRIjmM7kP3l74+lQiHehcNwDioXGw4H4GgCeVlByrZQ9Cev0NIAhIJbAPX1FRKCRnGfwq5p+m3GrahDZWyh55ThVJwKNgK4lUg7huI4GaY8hb7x4Feip8MYra1Mmp6k0ZAyREg/r1rgry1FjeTQM24I3ysRjcOxqYzjLRDcWtyrtPWgvuOF+maU5c5xge1IcDpxVCHnEOcnLEcGoCw+pprvyRmmEkjngU0hXFLCmM7EccCmGjn1qrCuB5phz0p9IelMREa2fC1/8AYNegcnCOdjfjWM3WhGKOrg4KnIolHmVhXs7nrfiG1W4tWOMhlryeRDFK8bdVODXrdjONV8PwTKckphvrXm2v232fU3IHD8/jXLh3ZuLNqquk0ZeaUUAZp22usxsAopQKXbSGIKdxilCGniPNIYxY9xq3GuBUar2AqdI2J6GhgOHvTJHAUjvUwhJ9qcLdB1OTUcyQ+VsihfgV2OharDBpzxzSBdpyM1y6oo6CnY4qXUGqZ6A3jeztoAsSNKwHYV2HjjWrm2+GWh39thJLg2+QecBoWavEM16948/5I94Z/wC3X/0navDzSTeJw3+L9DWMUkzzaG9ub28ae5k3y4Az7VZ3lptuScg5rOsSPPA9a6TS9HN9PwxAHU178HdGU9GT2iRNbw7QCxHJPrmrsenKJgzgVqw+HbZI9g8zcPQ1fg0C3MZMk8mR0FW0zO6Oelt0AGcdelZkkyI3AXg9BXbJoNihwVllJ7k04+HrIf6uEIT6ihRYm0cHJI7ANBGzZ7AVntrUau0cl3DFtOCC2SCK3vHd9N4WgtRaxRu9zuAkb+DGO3415GSWYsxyxOST3NUokuR6CNZ0nrLqIJ9FBp6+IPD8Y5nZv+Ak155UotpjHv8ALbb61XKLmPQB4t0OL7iyH6Rmj/hOtMT7sM5+iAV54KWiwXO/b4g2g+7aTn6kCoX+IKfwae/4yCuGoosFzsm+IM38Onp/wKQ/4VE3xBvj9yytl+pJrkaKLBc6lvH+sH7qWqfRCf6123g/WU13Tl+03lul8HKvGWCk88YH0rx+tLw6iyeJtLUjg3cf/oQoC576ujMOXmQevNS/2PCFy02fYCtZVidsJEx98VYFsSQCODSGZMWl22B+7d/5VbWxiQfLbqB7mtAQlAFB59BTZbZ2H3sewoAyUg5bbGnXFMulCgB3Cj+6orRjtdseeetZ98UjJAAz60AY11IpfCoeOhJrmvET4a3i9ASa6GZsyH16Vy3iGQNqpUdEUCoZSO3U/wDFlh/n/l4rz5G6V6Av/JFR/n/l4rzxTXhZJ/zEf9fZ/oa1OnoWAc1LGfmquhqyh5Fe0ZnQ2TbbN29FNUJWEaO27JVS36VYhfGn9fvECqt+NlnKc/ewo/E//WqZMaMyO3jZFznOOuanW19CDTYB82Pwq4pAwMde9cUjrRAsTKeB+FWUiDD5QfcY6U/0HBzUqEDnGcelIoRYvlzSlcr1H5VYEsRzng+lGY3bAI5piM+6txJYTbscjGaoQKxs3iLgtDhwMfjWpfTJIVt4ycDqaryRRJ9wgELjr2NVETKUsaP5cijG/hh+oqaycTQOjHO1uQBT0Ecmnq4OGXA4/Kq8ZEV+xThX6/WqEea+USMnH4mkKtnORipMKQSePTNJgH0xXWYBkbuAD3570uBz7VHt28dvanheoIAPXNAgIBwB1PvUiWuU3nge/FPDhWyihQeck5phkLnknjr6UhjDtCkRgnPUnpTQi4LE49zRnn/61LsJNK4xrrlc9PQYqWyu5bG8huICRLE4dSPUVCVIOByaeMIM9zQB7atxHq+lRXaHcroGGfQ9R+BrzjxVpqxzi4I+78p7fStn4d6uJI5tMlcEp+8jH+yeorR8SaaJ4JIiMhgRn+Vckf3dSxq/ejc8raRVHv25qs8hJ64qWWFoJWSQcqcEZqsevSu5I52wJwfamsxOBQevNNx3qhAWpMmikI5oAKX60gBOSOQOtLQA1lplTgZHNNZQKEwaO68A3pktZ7MnlOV+lUvF1pj95jlTn8KyfC96bHW4mzhZPlauv8SwLLE3TkVyyXLVv3No6wsecg0tWk06djgrj61ZTS1z87/lW7mkZqLZmKefWpkikc4CGteOwgi6Lk+pqYKF4AAqXUXQpQZmw2MpILcD3q0lggJLNmrGDS89ahzZSghiwxx9FFPZNwGBT8DPNKcjpUNlJFcqc9KTAqZ1yMjrUQHegBCRSUEHtSZwOTQA7HNeu+PTj4PeGP8At1/9J2rx8nFeu+P22/BvwuSQP+PT/wBJ2rxsz/3nDf4v0KXU8stGIu488DNeu+HrJYtMjkC5ZxuOK8U+1ojqd/Q17J4dvGuNDtwhwQg5+lfQ0tjnqnSwxhVy6nPQVaSCNlGcZ71UsrlyAkyEkd60ofLAPFbGIxbYK3ynFRzLJH907jjp6VbyOTnrTWUMCM0AeU/F6Jn0bT5zj5Lgrke6/wD1q8jr3D4uQL/wiKMo+5cof5ivDxTQmOTG9d33c81uyF3lSSKUPABzEODWEOD0rSOoRGY3BibziB9KpMllUWsj2st1gCNHCn1yasJpc7zRxKQS6byeyii3vIvs0lvcK2x335X1q5b6jH5xZIZSNpVsEcL2xRoGpTnsY0jYxTiR1OGA9auPoYVFiE2bopvKegpkUlu1vOtsmFX59zD5iR/SphrduXErROsrAK7L/dA7U9A1KMNta/YxcSmQ7SQ4A4z2AqnOYmlJhVlTsG61O1yos5IEBwz7gT2FUyaljCtfwq4j8V6U5Gdt0jY9cVj1t+D1D+L9KBHH2gH+dID6Ws5JLqESFDGp6A96s79jfeqKJz5XQAAUhbOTU3KLCEF8ls98CnTSgL8p2kiqUcp8w4z6U6UkjrQBHNO20jJPtWFfSEsa05nGDWHdyfO1DGiqBmRM/wAT1xuqy+bqtw/beRXYRtk7iOI1LVwkjb5nc92JqGM9LH/JE/8AP/PzXnamvRB/yRMf5/5ea86WvDyT/mI/6+z/AENanT0JlqxF1FVlqzCPmFe0ZmuGCwQIe5JqHUmzBAg/ikJP0A/+vTJZALuOPONqCm3zbriFf7kWfxJ//VWU9mXDcZCuKsAfMPpTIx2qUKWkwOAK5WdKEJ+bPYUqFgjH8KkeEAZXr3+lMJ/dKO5JbFAwjYqxYnIApqSModwcYU0nCx5P8VRyMI4Wz/nvSAqLKWunLE+maiupmEjbTwI+afboWOT07iq10fMmaIZBdwuM9h1rVIll2zjZLGJGPLkHiltV8y5Z+dq5PNTuREpPaKP9TTbNRFaM5HLcmgTOO/4RPX/+gHqRPvaSf4U3/hFfEIXnQNS/Czk/wr2P4h+MtR8Jf2b9ghtZftPm7/PVjjbsxjDD+8a4k/GHxEBk2Wl/9+pP/i66rJDlCEXZs44eFfEI6aDqo45/0OT/AApB4V8RnJ/sDVPxs5P8K7BvjJ4hVM/Y9LJz08qT/wCLpn/C5/EeM/YtK/79yf8AxdPQnlp9zkv+EU8RAcaBqn/gHJ/hSr4W8R9P7A1TH/XnJ/hXWj4zeIyeLLSiP+uUn/xdSr8YfER62el/9+pP/i6TsHLT7nHr4T8QEfNoOqfjaSf4Uj+F/EbfKmgaoB6/Y5P8K6t/jTr4bC2elkD/AKZSf/F1C3xs8SZ4sdK/79Sf/HKOUP3fc5ZvC/iGJfl8PaqzH0spP8KgPhfxOzZPh/Vvp9ik/wDia6//AIXX4n6/YdJx/wBcpP8A45QPjd4kzj7HpP8A36k/+OU0kJ+zfVnO6LonifSdXt7xfD2r4RvmH2KTlT1/hr1m+0u8urQFbK53EdPKbP5YriD8bPEv/PjpOP8ArlJ/8crrvCPxJ1DxDazfaILOO5iblY0YAg9DyxrGtCL95lw9nsmeceI/B+uf2h51touoyrIPm8u1dsH8BWJ/wiPiU/8AMu6t/wCAUn+Fe06z451uxgka2trFnUZAkjcg/kwriD8bvEy8Gx0kH08qT/45WlOScdCZQpp6s4w+EfEvbw7qx/7cpP8A4mmnwj4m/wChd1f/AMApP/ia7P8A4Xh4mx/x46T/AN+pP/jlJ/wvHxN/z46T/wB+pP8A45WhPLS7s4s+EPE3/Qu6v/4BSf8AxNJ/wiPifv4c1f8A8AZP/ia7Q/HPxOP+XHSP+/Mn/wAcpV+OPidmA+w6Rz/0xk/+OUxctLuziR4P8T5/5F3V/wDwCk/+Jp48IeJgf+Rd1b/wCk/+Jr0dPi54kA/eWmlZ/wBmGT/45T/+Fu6//wA+em/9+5P/AIuodRFqnT7nnKeDfEzf8y/qo+tnIP6Vaj8DeIG+/ompD/t0k/wrvl+Levk/8eem/wDfuT/4unf8LY1//n003/v2/wD8XUuaK5Kfc4qHwVrETBhoeplhyD9lk/wq5LoPiWbG/R9UbHrayf4V1A+LWvYObTTf+/cn/wAXTf8AhbmvHpaab/37f/4uofK92NRgupyp8Ma+f+YJqX/gJJ/hR/wjGv8A/QD1L/wEk/wr0k+JviD20PTP++v/ALbSf8JP8Qv+gFpn/fX/ANtrm+uYT/n6vvRt9Xl2f3HnA8Ma/jnRNS/8BJP8KD4Z1/8A6Aepf+Akn+Fej/8ACT/EL/oBaZ/31/8AbaT/AISf4h5/5AWmf99f/baPrmE/5+x+9B7CXZ/cebf8Iz4gx/yA9T/8BJP8Kb/wjHiDP/IC1P8A8BJP8K9M/wCEn+IX/QC0z/vr/wC20xvFPxEH3fD+nH/gY/8AjtH1zCf8/I/ev8xewl2f3HnI8NeIP+gFqf8A4CSf4U//AIRnXj10TUv/AAEk/wAK71vFnxL/AIfDemfjIP8A49UZ8WfFHPHhvSfxcf8Ax6n9awn/AD9j96/zD2Mv5X9zOFbw5ry9NB1Mn/r0k/wqJ/DniDn/AIkWqD6Wcn+FegjxT8UD10DRh/wL/wC3VOvib4kbTu0TSc+x/wDt1H1vCL/l7H70HsJfyv7jy1vD3iJc/wDFPaw30sZf/iagbQfE/RfDOr/+AMn/AMTXrH/CUfEjvoGl/wDfX/26nf8ACUfEb/oA6X/31/8AbaaxuDX/AC8j96F9Xl2f3HkL+GfFz4x4e1UZ/wCnOT/CvWfHuj6nqHwe8MWFtZ3Mt7D9l86GOFmdMW7BsqBkYJwalPif4jAcaFpZ/wCBf/ba6LWNc1yHwxp8+k2drPrT+X9rt5eEjyhL4O4dGwPvHj161wYzEYKpWoydRe7K6tZrbr2Q1hpa6P7j5/TwL4hJy+i6r/4CSf4V6f4T0zVbbTkin029iKnGJIGX+Yq+PE/xGzzoWlfn/wDba1NM8Q+LZc/2jpdlF6GM/wD2w16Kx+Fv/Fj96IeFdvhf3GjDBdFsm2lXJ/uHipIYLxHIaKQj12mpotXvCR5kUI9cA/41OdVm3gBIyD7H/Gt6WIpVXanNP0aZjOhGGs7r5EbxzgDbDJ1/umn+VMR/q3z/ALpqQahOR9xM/wCfel/tCX+6n5H/ABrfUz5aXdnG/EXSb3UfB91Da2dxPMGRljiiZmOGHQAV4p/wiPiXP/Iu6t/4BSf/ABNfRHiLxFe6VoV7e2sUDTQRF1EikqSPXBFeUj44eJf+fHSf+/Mn/wAcpq4nGj3Zx3/CI+Jf+he1b/wCk/8AiaUeEfEv/Qvat/4BSf8AxNdj/wALv8S/8+Ok/wDfqT/45S/8Lu8S/wDPjpP/AH6k/wDjlPUXLR7s47/hEfEn/Qvat/4BSf8AxNXbXwp4igiZ5NA1QpKChC2km4e+MV0y/GvxM7BVsdJyxwP3Un/xytr/AIWrrkdm0s1tpokAHCRuwH/j9NcwnGj/ADM8/wD+EX1+O3mSDw9qxJGCzWcg49uKpf8ACJeJf+he1b/wCk/wr0y0+K+tT2kUz2unqWUlsRSYyP8AgdZ+ofGbXrWcRQ2mmsQPmLQydfb56HzAo0e7OC/4RHxL/wBC9q3/AIBSf/E0n/CI+Jf+he1b/wAApP8A4muz/wCF3eJf+fHSf+/Un/xykPxv8S/8+Ok/9+pP/jlLUfLR7s4z/hEfEv8A0Lurf+AUn/xNbfhDwv4gt/FenTXGh6nDEkm5nktJFUcHqSK1/wDheHiX/nx0n/v1J/8AHK2PC3xa1/XPEFtYXFnpqxSbtxjikDDA93NJ3Hy0e7PTY4ZtgBjcH/dNK0UvIET/APfJqZdQlPVU/I/40v26X+6n5GpHy0u7IYYJFBzG2fcGiSGXnCOfwNWFvZCCdq4+lMe/lUcKn5H/ABp6hy0e7M2W2uG6QS/98Gse7sL1mJW0nP0jP+FdDLq9wgJCR/kf8azpfE97GeIrf8Vb/GjUfLS7sx302/Gm3hWyuDIUwi+U2T+lcYfDutqf+QRen6QN/hXf6l4zvbDSjdeVbF9+0Aq2P/QqxE+J2ov/AMsLIf8AAH/+KpBal3Zuiyux8H/sX2Sf7X/z7+WfM/4+M/d69OfpXBLoWsf9Am//APAZ/wDCvUV164bwCNeCwm5P8ODs/wBbs6Zz09+tcz/wn+rf8+9kf+AP/wDFV5WVwor23sm3+8le/R6XS8i5qnpd9DmV0PV/+gVff+A7/wCFXLbRNU8wbtNvFHvA3+Fbq+PdUPW2tP8Avlv/AIqpo/HGoueYLUf8Ab/4qvU5SLUu7OdfSNUkvnb+zL7BOAfs7Y/lUsukao99K39m3m3OAfIboOPSuni8WajM4VVtFye8bf8AxVUk8c6qQd0FnkEj7jf/ABVZVUral01TvozLj0jUh/zD7scf88W/wqeLStQUEmwusk/88W/wrTHjTUiQPItP++G/+KqYeL9Qx/qbb/vlv/iqwtDub2h3MptL1Agj7Dc4/wCuLf4VWbSdSLNjT7sDp/qW/wAK3/8AhL9Q7xWw/wCAt/8AFVCvjTUWfHkWuPXY3/xVJqHcEo9zFfSNR+UCwu8Af88W/wAKq3WkaoY1VdNvTk84gc/0rof+E21LOBBaf98N/wDFVUm+IOqRswSCzIBwMo3P/j1CUO4Wj3Mi30XVACzabeDPYwN3/CqKaHqz6krtpV/tGTk27gfyrqH8fapHbmRoLLIGfuN/8VUNh8QdZuw7NbWIAOBhH/8Aiqu0O4rQ7mbPpGqNbbRpt6XlfnEDcD34qabSNTFqI10+7JPHEDf4Vrr451RrxoRBabFUFjsbr/31W1oHiK71XVZLWaOBUWEyZRSDncAOpPqaqMYt2TEowbtc4740kAaHkn/lv0/7Z1xK2/h0+H21M2WqFluUtmQXsYBJQtuz5X+z0967T42Y26ID/wBPH/tOvNhqNsnhGawMmLpr9Jgm0/cEbKTnp1IrW2pNV2mzQ1LwvdG1tbqxgZ7drBLmQtIoJOMuVUkEgewOKzxo89zbaatnazNPeCUgl02OEJyR/dAAOd3pnpWrBrenR61pdy9yRFBpJtpG2NxJ5Dptxjn5mAz05qta6zCtpoSW+oC0ntUuFkkeEuqFmOAwwcqwODgHg9KdjN8pnJpF6NUi04RxyXcxAjWKZGV89MMDt/WmDTtRuLOC5itWMFzP9niII+aTj5cZyOvXp19DVrVNQsbbXbK90jyvMgWOSVoEdImmViSUDchfu+nOeAK6f/hJNDt9VuEguP8AQLVRd2R8pvnuFeSRVxjj/Wlcnj5R7UWEkn1Oct/Clze6E8sMYN7FftbSZmRY1AUcFiQud3vz2qGDw/NJFHA9vJDdG/NmzSyIkasBypBIO4eucdutRvf27eEBZeYTc/2gZyhB5XywM56dfxrc1zxDp1zPPLbTmXOtm8VQjAtHtUZ5A7g8daYWiYl94V1CHWLywhMMwtid0wuIgiruKgs27ahOPuk5rGk0u9i1Uaa8DLeNIIhGxAyxxjnpzkc9Oa7Z9Y0yO61y3g1O0MeoXK3kNzPYmWNCGk/dujoSDh85UHBHXk1z76l5njC0v7u8jnjhng3XEcHlrsTaBhAowAq46dqAcY9Bn/CIeIEA32O1dxRmaaMCJvRyWwn/AALGa0/CNvqmleLHgkgEfknyrtZZFQJngcsQPpzz2qndaraSaPr1us5Ml1qcVxCu1vnQedk9OPvr155rSudS0O+1XVLv7XbrcMtv9lmubZ5I8LGBJ8uD82QMbhjg+xoaUlZhaN7o9F1LSLm6WVUiy0Zw+WA2/Un+deb+J/Bd3b3Uc2nWxkie0W4ceam4nBLlFzlgMdgcV6bYa/Ya/p981rceaG8tATGULYAzkduf/rVia6+n2d/Y629yTLptuALdY23SMFIC56bckZJPTPBrlo2hLlNpxi0eaT6IZNL0N7KKWW81DzQ0Y5yVfaMDtx1qhqGj3umGL7VGgWUEo8cqyI2ODhlJBI7jPFdPpHiCwsLfQWlky1tHd29wnk7yiyggOAw2sBu6d8EY5qnqerBDaxSz6Zq1rCZHWCKza2iVmCgk7BGxJ2j24711mDjGxl6f4c1LVLU3Vrbq1uJPKMrzJGofAOCWIAPIx60638L6xc3ctvHZ7ZoZfJaOWVIz5n90BiNx9hmt22hstR8Et591BpcTaszKPLkkQDyh8oxub6Zz7mrx1zQLjVX1VZ47e5S8TLXVqZne3REVSgwVDkqxOcHkYPFBShEgtdDnuNA/tPzbdAswgaOWeOMg4JOdzA5BGMYyc57VZ1Dwxd2WpiyhMVwzIHyk0Z2jaGYthjtAz1bGetZlxrOm3Vhrlv8AbliL6o17bb43ImQhxtGAcHlfvYHPUVup4i0w6rdXcN/EYdSskhbfbGT7NIojPzqykMCUI+Xd69RWTgWnEyxoWpteNaR2paYR+bhHUgpkDcCDgjnqCf0qVPDOszbSlqrhyyoyzxkOQSCFO75jx0GTSSeKYobm+iuNRgnxpcltA1tbGJNzNu2BQi+rHJA61WsvEUAfwkTIVFldGS7/AHbfu184Nnpz8vPGaSgF4jbLSb7UEdreFSisELPIsYLHooLEZb2HNXNG8O3l/qFos9uUtnulgk3SLGxwwDhQTliAecA4qOz1cz6VLYi9s7OWO+luI5buxW4SRHCjAyjFWGz0Gc8nirUd9Y6nqeharqes+XcabcF7hfs7fv8A9+0vmKFGBndgg4xjjPSmoLuCaOv8dazqulXml2ukPJ5928iCNIlcuRtx1B9TXIyeJ/HAv47GKVJJ5EMg8v7O6hR1JZcqoGOckYrrvG15aWWt6RcXYfymju4S6DLRiRFQsB6jdmubtk8P+Hd+mzMJv7QsgJrtA6iPcVdABjOCAM8E89O1eJltDDSwsHKmpN36Lu+524mdT2rSk0tOvkihL4j+IMc80Rl2tFB9pYiOAqY8gblYDDDJxwT+lLb678QbmWzjS+Be8RpIFWGEl1UsDj5fVW4PPFbOm/YLizj0mLVrds2E8Al2uCzs4k4XbkKNuOcE5OBXTeEvDdpcR6NqJneaGytZI1JjKhi0khJ57YbjufbpXf8AV8Enbkivkv8AL1+5mHNV35n95haNafETUr2GObUvJt5OWn8q3YKB1HA6+3Wu30rw/rEKg6lrclw/dUhiVfzC10dusNvCkUcXlqoyqBcce1Pt8ACPLMdobcec01h8FzKKpx18l/kQ6lXlvzv7yotggHKk47k082EQx8mc/wC1VrermHB+V8np1GKIHVo48n5yp/SmqGCcuVU4/cvL/NC56qXxv7/X/IrfYIunl9f9qmtYR44XH41fUqYw68r1zimNKiZXY5KjcQF6CqlhsDFXcI/cv8ieeu1pJ/f6GDqej3tzbEWN7JaS/wALqiOM+4YHNect/wALHFzPDEXn8k4MqRwqjemCwGT7da9ikdA0eATxu4HQetV9MvFOoPbxtwxJ2jjdkcfyP50nhcC3bkj9y/yHGrWSu5P7zxZNV+IMsTOBLHtJXbNDCjsR1CqwBbHsDXo16LuTwl4WubWby7u8ltPtcgKZdHjJfaG4znnCjPHHGa7PzIZcM0BfA3qMZwOgP6cVS1fSNN1LT7P7RDmC0uIrq22HbsdPu8egBPFeVjaGCVWi0opXb0sr+vfvY1jVq3td/eeIv4n8Vz/apbK5D28MzxB5BAm5l5KruA3EDBwuTzWl4J1/xB4gvJ47q8QJHGZBuWNM4HTt+fauut/h9Z2EU1pcEXdpLcPdGOWLLx7gB8pHQ8DnIzxnNTW+jWGnxx+VEseVaLfg8bhjt74r0lh8Ck26cdPJf11IdSu2veevmW7OK6lQs43DJAwRyR6ev4Vbgh3seDkISAPWmwNCPJ/egeTxgA/NznI/OpY5UJkLHZvBAOM8mssPGhSxslTSV0tF+dlsXV55UI8zvqOELBgCvJ6YOc0rIyYz36EHNOSZEMShtwAILY9fY1HPNhVAYMOo2rivT9vT7/1p/mvvON0dH/X9dfuKWuadLf6LfWqLl5IGAyfUcV4Df+EtS0XTrqXU7GZHRYmjkSVNgDEj5h1OccY6d6+kknTZHu4LDDcdhkfyP6Vn6rpOn6/ZT2l5F5kLFcDpnaeP5VUa0G+VPX+v8wlSsuY+drvwrrVhbzTXNlsWABpVEqMyKejFQc7eR82Me9RroGpnTvt32b9x5fm4Mi7zH/f2Z3bf9rGK+g5fCOiLqN5eNbgzXcTQyZzyGUAg/gKsf2FpIw62dqrCEWxb7OpbZs2bd2M428denFP29PugdCzPCLPwndLp2pXeowGJYbH7REolXeGLIF3IDuUEMTyBmq+mnUdUjnhsIEHlhWL71SOJQf4mYgDJ9TzX0AfD2jyXN9dG0jaW7VlmJXlgSCQfxA/KmQ+HdHgtJrZbGFIpGV2/d8EjOM/maFXjy899BOj7yijxYwLaeH/MaKd7wXzWhRSrZbaG4AyDye1ZGs6Nqsc0Ty2RVHYQoEkWUh/7rbSdrexwa+ibfTdNtI4lhtogI5/MUCMDDDjP14qQ2Flt2CCExFwWVIVXJGeTge5oWIg9EwdHTU+bbjwtrNs9sj2e77TJ5URjkSRS/HykqSFPI4OKL3wrrlhbC4udOlWMv5Z2lWKt1wygkqeD1Ar6XWC0CpEkcYCybwFTAGKjMEPkzlUH7xg3TrjPP60vbwtdO/8AwAdG2h82WnhDxBqFs1xbaXM0YJA3FVZyOoVWILfgDXrXgTwDaaNYR6pcoZNSYAZZ8CLI5AUHr9c13UaQsICflaMZwB754pdyeUwAwTJnpxWccTTlBSbtct0OV23BYP3fmZHBx1qSWJkZsD5R3zSF4/JZCcEN3HWnGVTLMQeowOPpVRrU5Oyf9f0mJ0rRu1/X9WHCNkj5HHfmoXjZlJA4+uKnkm3xyMGAz2281CsibAJG+XqQRz+FT9Zp3s3b+rA6GuhRlt5pI8oud3TkDP0rLFoss9usiuBIHycjnAPT0/Gtmfy/Jtnkk2BSTjBJPPas+S5ia4tJncJ80pYYPG4n296t1YWTvuONK2/9bHI+MgsWh6ai53Slmb8DXK6Vp4v9Tt7Z2Kxu37xh1VByx/AAmuz8T2sF8LO2a9ii+yRyNMNrMwXghgAOR+o61l6Otvp63lxJDC0p03McJLkPv4ZiRgjjIwCPvcVDxFPo7i9i+bU9Hs9It3+HVvp6uzWsjLIpZwGMbTBwCeBnBxXn1zYTQWsV4EtlSZ2UQQXCvtwQBt+Ylsk9s4rv7G+gh+HUF1KqW9uixEhNzKg3r0yS3864+KG1tZNNWG7iaTTZfPkjcMMqShzu249f0714+U1KdN11fepJ/hF/hextOldf1/XcrDTrlXjVogC7+WAHVsN/dODwfY4q01lLbSLHIgV26LuGfx9PxollNrc2m29tpbR7hJh5FqIyVUnlsKORk8c1YSynnvJJTIjrIxkUZOSCc+le069OKTbMHS6JFmy02fzXLRkeU+x+RwaraxokthdKVici4mAjbcu3kfdx1znPPSu1tDD94/xjfISOjYGf5Vx3iLXJtR1HTY7a4iWCLVokYLk/KeMtx1JyBjjpzWdStSkrXNIUnF6FZdLuop4zLCdhkCMFdcgnseeD9anXT7mV2MUR2K5TLOowfQnOM0+G6srW8vwt8jj7WokO1gIgshODkctxjAz35p80iyWdxEJEUi5MmWzjaw+U/jXM5047v+v6a+835Cn9huXMsSxhXj+V97BQpzjkkgVWuLWayeSGYBZBwQrBsfka3Xmtrg6nc/aIGhknVVEwfYTyf4RnPX9c1Bc6dJOklxPPBuLK4dM4cNgADjj8u1TKUVHmGodEYcsMqQRSBCROSkZBHJGM/wAx+dVV0XUJycW/KyMuN6/MwPIXn5se2a6KzSNPtgMsflWEguI3OcK/KhTx3O38qpac1vfS6az3JjlsiVZApPmAOX3KemeTnOOmaqnODjzXBw1sY99a3Lw2sMUeXvGEcAyPmO7b+HPrVnTdPlVIreJC8pydo74rXiNpe6xpk7XsSmwkeaWNFY8GTcCDjHfH8s060jEWqrIZEEfluVfPBGCP5mh1aatdk+zZSsNF1D5pWgGZmOD5i49MZzjPt1rb8FQyjWbmZkITymQk+oZeP0NVrOGGCx020My5WZpMjOCML049VNbfhlrWXUryaAqWZpMNg/MuVIxn0JOeO4rWlUhKWj/r+kEYWkjiPjo2P7AHr9o/9p14/jJ6k/WvYPjp/wAwDkD/AI+P/adeQfTJHrXWYV/4jFLcjpn2FJk9cfnRznnik5zyaDEXn1pOepOaecAc1H0oAcaTNJmm5JNAx2aMZHNAGBS80gIWHem8/hU4APWkZeTigZ0/gTVPsGs/ZnbEVyNv0btXda9aLLCWxwwwa8eid4ZUkU4ZSCCK9ksL1NZ0CK4GCxXD/wC8K5K8bSU0a09VynkN5bm2u5IiPunj6U1ETnzM9OMV0Piex8ucXAHT5Wrn8EjJPFdMJXVzNqzJRdz/ANniw8z/AEUS+ds2j7+MZz16CqDkbqnlJCcVTf8AWrSE2MZjnpRHO8LbkOPbtQVpD71RmzYtZoLrhx8+OuKthXjOFAx61zkYkVwUyCK27W7JQLLgN796ynGxpCV9ybbgkk5pGPOQuKkB3HhRj1NKwwOWGPQVBod/8VM/8SnGP+W3/slcm15puoQ2zX6XYuIIliPkbdsqrwuSfunGBnB6V13xRcINKyAf9d/7JWF4Q8Iaj4rusQqIbND+8uGHAHoPU15OUUufBU/K/wCbOvGT5a8vl+RL4cu4w9jDZ218Z4ywlit0UibJOGZs5OAQMYxx2r3PStNSHSkgjwIjCqLjthcVnafpWm+F7H7PpcSq+MSXL/eP4/0FaOm3ghUROeOqMRjIr1I4Smm29b/8H/NnDKvNpJdP+B/kXEs2VWBihUlcBkHX9OKQWsqMhQpkRhGznjHcVfSaNxningxk9cU1haaSt0I9tIzY7N1FuOCYxhvy7VHLbPDaxgFfNDbVx3zx/X9K1TJEO7fkKhkmhDByq7h0JHIqZ4SHLyx06fl/kio15Xu/63/zInt2+z+XFgEAAZqOWPbJK5ZcNGFH15/xplzqcafcz+JrCvdTZsgE1tKjBu/9df8AMhVJJW/rp/kS31+tsmFbJ2BGx7elYcMu2aa5diuR0B9KZLI0jZOTUf8AZ95qBEcSMid2PFZfVadrf0uunzVy/bSvcs6L4klnl8u5hjkZVCglQwwPY9K7mQxTaagYrGJEG0gfKCRkfhXJWeiR2KbU+Zv4m9a6O93R6Rabeo2D/wAdNeNmmGprE4dJaSk/yLhVk032LKxSwziZgpRk2kK2e9Ymr6f5Onhs8ADdt9fatTT7vcmx/u9we3vRrIVdMuVPICZH9K9r6tB7/wBap/ojP2slt/W/+ZytvJvfGScLySOtOaYKSOc5plsuJM9ttNkYeZ75NeZRw8FmE49kn+P9bemx2VKsvq0X3bLiSjjPFSqRxwPzqmjZ64p4JPeu54PllFwe3fXt+it09Tn9vdNSW/8Awf8AMtgHIJxx+tSRkoAKqpKR15qwrg963pYaFJ3j/W3+SM51ZT0f9b/5koLyqpcjGc57ml8psFcjaTn3pEIAIxUqEEe9ZwwkeVKTu7Wb76behUq8nJtAilWcZG1ueKY8LMW5Ug9Ce1SgjPNOyO1aTw0Jw9m9v8/+HJjWlGXMtyu0R3OQRz936/5FO8tgigYJByRnrUjE+lNyT2OaX1Smm2uv+d/68tA9vPRdiNkKJxgNu4x78VLt/d7QB0wKFw+CQNw9qePrSpYaNNu2z0/r1/RDnWckiJI2BBOPugUhjYKU42k5z3qfj8aXHvVfVafLy/1ruhe2le5WaIsCOMls/rQIWEhORg4qYKc80pAxwOaqOHhFprp/wf8ANidWTTX9dP8AIhZG+ccYbkVBKuQOegxVtge9VZuATUvC02nF7f8ABuNVpJ3X9aWM28kYoqEnC5wKoI6l9p3Hj7pAxn1q3dZfn9KqKm3c7flR9XjyqHRf1/XUftZX5jlPEV7b2+r3pXzWuXi8nBACKCBk5zknHbFZy6jCz8rIFNiLYnAyGx169OKr6gzXGpTyMxJLdSajEfpWUMJGMUpatIqVdtto9KZml+C7NgAFUUc/3ZlX+lcrHdQ3Fw5CTEXESxyrgfKRtwVOeeV74rsrS2ef4PxwKMs3/wAkZqnpGgJbIryrlq8nJqEZ+3v0qzX/AKT/AJIc6slbzSIrHSVligUqwijBwWHJJOSfat6K3RAAqgY4qdUCjAHAp230r2Z4aMko301v533IVZq766fgNcOkEhUAttJA9eK89LQRIfsySbzKkuXA+QodwA9ecfl0r0YAjFee6jPCNcuURQiiQgehrOth4WTXT/h/wZdKq7spPdm2ur4xyXYiu5fPMkQAdGyTjAPI+b1FMh1WMieGeW5dJCrLI2C4IyOQT6H1rWWGCRdxQA+tI2jxTnIVXPoeD+dc6oQ36/5W/wAjd1JFa31lbe3ubeMsAzB1LxI4bAI+YNwPqKrS+Ic24M7u8izBycDGAAAKtS6AqqzBZYz78iubudIul3/vYygbjgitPZRejJ55G6viW2aWWOGa4WGV/NZRCh3Zz8rZPI+p/CqVvq6wag7LbkQuXygP3UbIwPoDWdb2V1BOQYl5wFO4AVYk0vVi0jfYzhuAd46UlQgk09bq3yB1JM1rG6trQSeWtw8cyfvJJAqt6ggA+w70k/iC3tbYld/ETRxsQNxYkHOM8D86pJpmpXkWxfLT235P6Ug8NzIV+0XCYzjpkih4eDd5P+u4e0kloS6dqbyajpjyeY7Rq4kLd8szDH4EflXd+DJk/tCa3QuUWJnUnjOSucj/AD3ribTT1s92W3Nng/1rr/BH/IZm/wCvdv8A0Ja6IYeCfN/W7f6smFWXOl/X9aHO/HPA/sHI/wCfj/2nXkO/jivXfjoMjQR/18f+068exzXQTX/iMePpxR2xR6e9GcDPXPSgyE60jHAx3pegyaTBPPQUAMpQOM0oXmnEfjQAgHelIyeKeqg1IY0CK28Ek8rjpSHYhVO5p+BS7m5QdM0/y8H5utIZF5e7oOK7TwBqIjuZtNkb5ZBuTPr6VynAHJwPSnW161new3MXDRsDms5rmjYpOzud34jsN8cikda84mfyy0bD5gcEGvXLx49R06K6jOVlQNmvMPENp9nvfNA4fr9azw8vssdRdTIJJppGOSaVnVeD19BVu10yW7IeX5I+w7muptLcwKUcUlw+yJSfetez0hYyGk+Zq0o7eCyh5wqj9agaWa7bbCCkf941nzSnpEqyjqyrfm3gGVGX9AKxj511KAFOSeABXVwWEaDBXcT1J710fhvQLeS7Egtyz5444rWMOUiUrnDJaXttErXMDhT0Yip/MRlwSQR2r2nV7bS4NNJ1VoYo1H8WBivC9bvbaTVJF00k2oOFYjGaThd6DU7bnt3iPwvN4p1vRrVflt4/OeeT+6vyV3lrbWum6clnZRiCyhGAFHLH+pNV9PcrFOqj5nKqP1rS8kM6r/AnA9z3NeXkn+4Q+f5s6sw/3mXy/JFSK1a4lWSQYUfdXsKsTacVYGMZVjnB7VeijCjpVlcgcDNescVzIijliBG0jmpPMfvmtVY1x8wpfs0belAXMVpHP96q8gduzGuhNpAfUfhSfZogO5osI5VraWQ8IaE0OWU/PxXVeWqjCjio2zyOgoAxodFgh+8ATVoRqibFXC1aIzTCtAFN4QO1WLtN1hCvpt/lS4zU0q5t0Hpj+VeHmv8AvWF/x/oa09pGbBH5bFj0xVLxBd+XYx2xP7yQDcPRRWhd3UNlAZZT/up3Y/4VxlzdyXt080hyxNe2Zlu0O4A/7OKglOJm+pqayHy5+o/lUM/Ex+pry6P/ACMqn+FHbP8A3WHqyRCQKlDtjpzUcZzU+MjFeqcYKx74qQN+dIq8U8LkcDNAE8Mm44PWraghsjkVSRMfWrUL44NAibGaUDPWkC7qdtwaYDDuzxQMjrUgFLs460AR7ie1KGOMYpxwOMUowRgUAN3c9KVcsacFGacFx2oAaRTepqQ0lAETntVKcjBzV5wKzJ+ZD1oAqSDnIqld/u7SaT0U1elKRRmSV1jjHVmOK5nVdT+3Ygtwwtwc5PVz6/SgZys9pIHLD5snNXtL0ie+lCqhx3NdDo+gS3zh2BWIdSa7e10y3tYhHHGB70rBcfp9mLLwdFbdQgP/AKMzVVRitt4saOY1/wA/NWQVIODXh5J/zE/9fZ/oaVOnoIBS4orK169a2sTHEcSy/KD6DvXtt2V2ZpXdjA8S+J5Az2OmuVx8sky9fov+NYqRJNGN4zx170yS1PJA5zUkBKcY4H6VxyqOTudSgooUQ3NupMT71z0NPGoyIPnVo2xgGrKnMY/3qkZVIwVBA4qb9yikupyA5M7f8CpJ9V8zI3xMq9OnJo1KG3W3I2BWkO0Edh3NVP7MtoLdAq7ZCOn90GjQNRs+oZtmJCfMMVradqEg0uIXLor4/iIz7Vj2OnoweT5TuPBI7VMbQm8jUFeAWOBVCL0d3i+efc2zGOB944okuvNwdpxjdz+lQ3CMoVO5OKo3l35c0SKeGbn6AcVUUrkybsXdxJ55NdT4IGNXm/69z/6EtcxDtZA3rXU+Cx/xN5vT7Of/AEJa6TOj/ERzPx0OBoP/AG8f+068fQcE9q9f+OoydAH/AF8f+068hJwMYNBpX/iMUHuTRgCgc9qcBv78DvSMhFXefapWTKcUgwOB+dPBxgGlcdiEccEU8LjjvUjRhxwefWgRlR8zUDG7FNBCjtk54p42g9CfrSbwMccd6AJYpJLZSGjUq4/jXP5VAZF3bR1NE8yl2AGFHQZJxVeNsyCu7LIqWOop7c0fzRnUfuP0JWkUHBBNAdW/h/So5B+8OelLscnAU17+LzXGRxVSlRpxaU3Fe6u7svwMI04cqbfTub2hxanqm60stRaFYxkRtM6j8AM07UdD1KIH7VMs2OcF2b+YrT8I+G76G9jv53MIH8JHUV3F5pKXWNzDb7V4lTijFRlpGH/gCOqOEjJa3+88itNKlupMW8Kl/Yc1JdW19ZSLHKJVY9AM1395cadoEXkwIrSn+Edfxrlbm/mvJhJcPkZwq9hWtLiXG1JJckP/AAFETwsIrd3Mv7DczAF3B/3mJqaPTb44CTD2Ac/4Voo8YZ97BAmMkniq03iy1sMrYwi4n7O33R/jXZLPsXF2cIf+Arrqc/so9y2PDGqpaNdT6hb2sKjJeadl/pWF/wAJDfWUzRWur3bKON8EzhT9M4P6VnajqWo6xL5t/cs4HRc4VfoKp7hGOOKFn+K/lh/4Cheyiad5qkt8Q17PPckdDMxcj8zUMHl3EmyGAs3sorPJaVGYdBjirul6tcaTKJYViYg5AkTIqpZ7i4pPljr/AHV6foHsonsvxHn1Sw06xv8AT9QntI0mMUggmaNmZhlTx1xsb869M8LvJP4U0eeaRpJZLKF3dzlmJQEkk9TXl/xSuwmgWlltJMt0JQ3psVhj/wAf/SvUfCDA+DtDVwFP2CDB7H92teNlqX+rOFlZX5p/+lM7cf8A77P5fkjZXipgccU0J7Ui/KSTmuY5yUU0ynJEal2HHoB+NOHI4P41gXsmsW0TRiPMS8B4QTuHv6UwLOparNpqku0DMekYBz+eaSy8QwXNoZZUMTr1Xrn6Vyaz+YJWu4T5h4TMnI+oqs8skanyjkDnYeje1TcdjvYNThm6qyg96tOCD6gjOfWvMNR8f6bolmrSRSNIwIWLO0kjtmup8D+LrbxXoqyQtH5yEgxhyxX2OQDnv0rTklyc/S9hdbHRGmYp7yAdSPwrPudWggB+cE+i1AFzaAOaS6aYWgNvD5r8YXcAP1rlL7XbifKRHYp9Ky38Y3dtpX2mK5Z4I8IAI1zw2zuPWvLzHAYvFTpTwluaEvtXtd6JaGkJRjfmNS80nW72YyS2+T2/eLx+tV/+Ec1YOCLT6/vE/wAaB4j1U/8AL3/5DX/Cnp4h1Q9br/yGv+Fc3/C3/wBOv/Jyv3fmSx6deWMLG5h2KzDB3A84Poar/wBjavcOzrZEISSj+YmGU9DjORxSpqepXdy0F3N5sAG9TsUc9Ow966KKad7ONIrkxkIAMKpxx7iuOn/an1yfL7Pnsr/Fa3S3W51z5Pq0d7XZgpo2sLw1j+IlT/GrK6TqYH/Hof8Av4n+NZd3feJ2u7GG21a4he8EjJHPawgps67sKfWqFlf+O9QsIry31TMUoJXNvFnrj+77V6cqGfxjzNUrf9vef+T+4470vM6caXqI/wCXT/yIv+NSJpuoKf8Aj1/8fX/GuZMvxC/6CP8A5Lxf/E0eZ8Qv+gj/AOS8X/xNZf8AC3/06/8AJx/u/M6oaff55t//AB9f8aUaffD/AJYf+Pr/AI1yok+IP/QR/wDJeL/4mkebx/HG0j6nhVBJP2eLgD/gNNLPH/z6/wDJw/d+Z2MVpfKPmh/8eX/GnPZXjNny+P8AeH+NcZbXHjy6toriHVd0UqB0b7PEMgjIP3atRp49Iy2qf+QIf/iaGs8Ts/Zf+Ti/deZ1otrkD/Uc/wC8P8aT7NdD/lh/48P8a5kQ+Oe+qf8AkGH/AOJp4g8b99VH/fmL/wCJo/4XP+nX/k4fuvM6I215/wA8f/Hh/jQLW8/54f8Ajw/xrnJI/G0ULyf2mX2KW2JDEWbA6D5etSRQ+M5Ikc6rs3KDtaGIEex+XrTtnlr/ALr/AMnD915nQfZ7z/ngP++h/jSfZ73/AJ4j/vof41zWkz+K9W0u2v4dW2xToHUNDFkD3+Wr4tfFmedZX/vzH/8AE05wz2EnGXsrr/GF6Xmaxt77/niP++h/jTTa33/PIf8AfQ/xrPW08Ud9aX/vxH/8TTmh8QxRNJLrShUUsx8hOAP+A0ks8f8Az6/8nD915lmS1vwpxbM59nX+prGvYfEf3bPRCSf+WklxFgfhurVsxq13aw3Ka0WhmRZEIt0GVIyOq1oxLdx/6y9klPuiD+QoazxOz9l/5OH7rzOAk8K+Jb2TzL2Auew81MD6DNa2meC5w4e8QKo/gDA5/KuxEkvduPoKitr2S5tUmMMsBYf6uZQHXnuBmjlzy1/3X/k4fu/MbFYeSgRIwqjoBin/AGaT+7+op3nyf3v0qvdamtmkbTy7RJIsS/LnLMcAfnQlnsnZey/8nD915lt1KWRUjBH+NUWjVuopy3ss808DRSqse352UBXyM/Ke+Oh6Utb5Vgq+EhU+sNc05OXu3tql3FOSk1YqyW+35geK5PXctfAHoq8V2+Aa53xDprtH9oiXJXqB6V6FVNxaQQdpanItGQTkcGo1iVX6VZPzIQaiDZ68ivOvY7dyRbZXICnac8Y6H8KQ2k+eAG5zwcfzoR8H1HvVlnR0AO6rTTJaMZ4pLjUsPG22EDgjgkmjU28tDg4Kj07mrhuoYz5kXmRujFDvGN3uMHpTRqcGpi5geImWEhS3Y5Gc/rWvsZavtv8Al+pPMVrcLHbIvtS2sby3MrKjE5CDj8f61Y02dgz2czjIxsZupHbNJeTpps/2iWQiLHz45wR0NKMW2ox1bC5FJbu06mVtinI46g1SezijEiGMbyRhm5+YdD+Nb1yiSggAYcbs1htd/aLqWFgfMiAVmPQ5GR/WqjGUk2umr++35sTt1NHTrKW/YGGM4PJ9jXc+H9IGnO0jHMjJg/mKy/B8RSymbHDSHFdXB98/SuiK90zp/wAVHk/x0OBoOP8Ap4/9p15CoyOmP616/wDHMAjQc/8ATxx/37ryZY8AM34Ci5pWX7xjQmfvHj0pTjp+lPIJpmMnA/OkZgDg809WB64FKsa9xT9iEfdxQAwEA9cilLMenH1pzbUQ7QCaqOzucNwPQUATF1VTySagZiwyTn8KQgLxTeSaYhOSwJPI9aliC5yQd3Y03y9oy3ApUJaRcEhR2rvyv/fqP+OP5oyqP3H6E6RqXLHk1eWJhCxKnaELZ/D/APXUdnYzzP5g2hO2a1RFPH/yzyP9k12YrGzw2OrWjf8AeSf4/wBa+ZMIqUFr0NoXsd1HdSXjMbkKBa9dwwox5fvuz0q5qt7dWkMjhcXF7bLExUf8tQcZ/Jm/75qv4evDHMYZCUDf3h3q5rXifTNKUxvIJp8cRpz+deUse1KEXC6XS+miVraaK6Ta1uzpUVZu5wjDc7b3CkABSw5Ax2qC71CHIjQCRxz04+lVb/UGu5naJPLVySFB6VTWILy/J9K61jJynTqS+x5rsk9bX1t1vqc8mkml1LZujPtMitluNo4A7VXiCopCKSc84NRSSDGP0FVmZmOBXTHMpRa02VlrqtEtLry7dfmZcty67tIjBVYYBPHIPtUZyy8KxTZzzwDj6dc1qaFqWn6eS95YyTMo+XYwAJ7ZzWZqN79uv5rnyo4vNbdsjGFH0oeZS7a2tfT/ACtbyt8w5BIdiI+7JAxmpba2uLq4eGEF5XI24FLpWlXWrXKwW6E+rdgK62Saz8LwG0sds1+wxJL2SsoY9wpxglrFPW/fm/K+nbXuPku7nT/FbGzSf72Zcf8AjlaXh2/1LVfD8cdsW+0aVpkghXHSZ1Kxke+wcf79ZnxX6aR/22/9krrPBtyf7E08DO028an8FFGS4pUuGMK+W7Uppf8AgTv96bX4nVmEb42fy/JEujCwMF69vrdvbRyafJFcR2No6OhbGJHzIx8xeeozya6DwVfWare2drBpxjg8vN1YErFcEg9j0cY+YZPUc1OW5xTs/KQKitj3VhKMk9bdV09Er/P9DnULHL3uqWc/ia2u7SK3tryLV1gkADPcsm4oS7ZwqMOQuCCCMHtTJ3eK6u7WPzBF4fuZNRUKeNrukij6bGuF/CunETE8salChehNbLNEre5ol1f/AAOza+YvZ+Zys4sJ9L0/+0ILMNq0lxerc3xJjQMwKLtX7zFGUAEj7px6VyhFzpuj6fqjS/6tJbd3CHJTcdnDc53KBzz81erZ9zWRrWktqwhjadUt0YO8fl5aQg5A3Z4GQMjHPrThmEJ2hONo3bet9Pe0Wmmjsul0mHI1qjz7UvD0EPhhmui0s8aBzk4Acn5jx7k109vZaOvigR2GlW0EUdgriBV+QyiQ4dh/ER6nNWHUoxVhgililaFwymuaOYVFFrq3J79ZW/y/EfIjCsZEd9KMLv8A24ZR/aGc+ZjB8zzP9nPTPtiqdppts1joMpVvMuJ3jmbecum2Q7Tz935Rx0rukmEy5FNcZreWay15U1fz8pabbLm0XSxPIc1pECJDqFqqjyI7uSNIzyAuAcY9OTxXPpDaR+FbuGFI47hZds6oArj97xnHPTpXdygVUkrGGYOM3K28oy36x76a3/MrkOUv7drS5voLBTHG1vDJIqAnI8whzgHJO0c4OTitXQIYUkuHtry2mhbb+7to9iIwzk/ePJ4z9K0kPJqdaKmYSqUXSa3td6a2tvpfp3BQs7lDQlto9dvFiltp5ZPMkZ8ETx/OPkb1Xnjp06HrWBNeJDru6zY/b0v5N7KTuEe9twP+zjt06YrvbU5B+lcrKhivLlcgjz5Gz9WJ/rXmUMd/wp1p2d+WK333Wum3lodlSP8AskF5sj0HWmgfw6ZCAkSzqCfVu36V1HhzVYYfAe4o0zW8EpaGM4diCxKjHINc4DVq2u3t3DIxr1a2OVXePW+/nJ/+3fgcSjYltDb+fqwsZLL7NPoksrpZK3lhxxyxOHYBuTgHpkVYvNLhtLbQmka0h06e3Z7qa9haaJ5yqbTL869t+CTgdMdMdTouqrdRhWPNbgINbPNHzKSX4+Vu3TpoLkOBuVsrbStI0+8nsr2CbznivLpG8hQG4QJuO44bCgt0U4NaOh276j8OVgnT7SWgmiCyIcttZlUENz2HXniuuxmnKlZVMc5QUUtebmvfrd9LK2/TTTYajqebWCaJ/Zvh/wA9bVdEFu4uwqhYhd7I8ecBxnG/73f3xXVeE1kGjSkCQ2guZfsXmZ3eRu+XrzjrjPbFdEMDkmkWTcxHb1pYjHe2g42e99Xfq3231tfsCjY8qhgtNe1CyjtBbWp1GG4inihDtLG2wsvnOTy6soOCMgg4JFbVpcajrunaxrFqjLqEGn/YoFA5WcJvlx77yq/VK7/8aTitqmZ89kobbXd+t301urL0QlA80lTTPLf/AIR05i/su5/tHbn/AJ5jZ5uf+Wm7PX5sbqtFdN/tGwXxAR9i/sqA2QlB2GXnzNuOsmPLxjnHSvQPxpvzHqwFJ5lfSz9b+999vlttoHIcfoyXq/CqE6fvF8NObyAB82/acY98/rVGVdF/4Rq4/wCEfYmUiD7f5G7z/J8xfM399+3dnPzda78qO5pAqjpWax9pyly7y5t/O9npr5bD5DzPU0sTaauNBcDSPs9v5htyREJ/PH3COjbeuP8AZzzWvLo9pb63rel2tsv2afSY5Wt8bleXdKN2D1Y4HPfArtsqO9GV9auWZSceVJ/f/h1emrXL+Ich5pBPoA8O6FZR2+miKaImaWZf3Mc6om5WRcbpTnoSDwaXSoE1K38J298Gnj2XiOr7hvVTgBgTnHA4PpzXpBxnNMZR2qpZno1GLvdu977qS7f3vwFyHnFnZQW1jp17GrfaYtca1jlZyWWETPH5YyeF28Y6UaZaQX1t4St7lPMhaO63RkkBunBHcex4r0M5qMmiWaSld21u9b7XUl+HNcOQ4Rbi1s/DZs7iGJ7QavPax/aHIhgQO5Xf6qMYAPGSBxWcILOXw7m7jt57Kz1xQGaAiOOElScK2dqfN64x7V6VSGiOZ8rbUXdu+/8AwN+l/wAA5DhLr7viD+z8/Zd1jn7L1+z7V37Nv+xnp26VU1IWZtdVGhkDSjBb7zbsRGJ/OH3D2bb1x/s55r0QUufSiGZcjT5drdeyjvp/d07XBwOd0u0g0zxhe2tnGIYJLGGZo1JwX3yDd9SAMnvisjWvsJvtd/tbeb/A/svrv2+WNvk4/i8zdnHPTPFdzmkzisYY5xqe0abdkt9dLdbdbfcNx0sef2/hlr/W9SmcKLyBbclD90OYhuI9DnvXM+Jbc2129pd26RTCAujygtu68IB1PHXryK9nDYpJI4p12yorqexGRWX1q9dV2noklZ2tZJf0rdS02o8p4h5sEktu2qkNC1rGYjJ90uc7j/vfdpzy2g1G4N7gvlDa78/d2jG333Zz3r1a68Kadccxh4G/2G4/KsS78EXO4tDeI4znDpj+VRLFO7smtLaStbVPTTS9te+uxqpLr+R5nNIWV1lJEBvX870x7+2cUh2q0627KtobhPMO0sm3y+4yMrux7fhXcHwdq0TgiOKTBz8snX8xSN4b1gOD9kPHo61rDMpwvaD+/TdO9rfFpo/TTQOWL6nCSKn2u1ijnSSN5wQYgVVQR90cnjr371bvoo/7P1aBIwI45EdI8cLlVJwPxP51048I6w9+s7WyhVGPmkFaS+C7+5OJ5oYlOMhcsamOY1lJOz01368yd9EtbK17dQcYW3ODurq0e2JtUt4ngCmNlBLN0OUxwo65PrnNbeiabPquvXscQAHyOzHsMYz/ADrvbPwhZQBTO7zkdm4X8hW3HbxwRhIVCKOgUYqquLVSn7Ple1tXd7xfZdY/iZ3s7r+tzj/DltpNnevDfbBraXcioJM+Zsydmz/Y2Y6cZznmtjwrcwpe6tYNIq3a3s8phPDbGcENj0ORzW4Fxz39alh++fpRWxaqRkmnrbrtbtpt5fiVQjapE8r+Nq5bQc9vtH/tOvJTnr+Fes/G9tv9he/2j/2nXkgBYZPT+deczprfGxwG4+gp6hQMAUzGOpNBfPHb0oMh+5RzngU3eWBPSl2ZALnPoB2pG+XrwOwoC5GFJ57UhHP3fzqU5C5zj6UbAq75GwP500JsgEZfp0prOkXC4Zv0FEtwX+WMbV/nVmy0t5yGk+VP51SRDZWihluZMAFia1G05baxeRuX4/DkVqW9tHAgVFxUepMq2LqWAZsYHc8ivQytf7dR/wAcfzRlUfusk0pc2EX4/wAzU91qVnpybriQbuyDkmuXm1q6t7dbS3TZtzmTGSc88fnWSFkuJSzMWY9STk08zV8dW/xy/NhB+6jfuvFE15mOLNvD6KPmb8ayBFukaQ/KD60+NI4VzwWHc9BT0We7k2woW/2scVxJJDbbImZUGBwPXvTDuK5Pyr6nrVqO3U3HkxkSzdz2FTanpn2e3R2l/eMcbCetFwsYzHcdq8CpEjAGTSiNU5NMZix2jJ9AKYgd+fatfQvDdzrMu8jy7ZfvSNwK0dD8Kb4hqGrN5FovIDcFq1brUGv4xaWKG209eOOC/wD9ak2NIS4vIrOH+zNDUKBxJcDqfpWJFps8915MatJIx+pJrqNI0Sa9lW3tYifU46V6XovhG20mMTFQ9werEdPpSA5X4pW6totlcn78dx5Y+jKSf/QRW/o8LwaDo9yn3Xs4Q499gwax/ih/yK9v/wBfqf8AoEldV4eAbwrpIIyPsUP/AKAK1yv/AJJjDf45/mzpx/8Avs/l+SNRJd4VvUCplJqngK2AcBe1SCbsOlcxgW84pCTUIkJp26gB5Y0lN3UhagCnqFp5i+Yg+Ydfesg8cV0BbPFZl7bbW3qOD1oAqxSmNsirolEi5FZ1PjcoaYE8vNVXFWi24VA4pMCBRjmpVpmKevWkBdtep+lcveMRf3OP+erfzNdPaffP0rl7wj+0Lkf9NW/nXmUf+RlU/wAKOyp/usPVjVYmnbjmox7UvOetescRr6PdmG7XngmvQoJN8Sn2rzPT1Z7uMDrmvSrNNsKZ9KEBbUZpjFgetSBhSPg0xEe40u44600oaNhzQAvmEcc0GRh16fWkKHFIEbPJoGP3k96QsfWk2mjYTQAFj60g4PU/nS7DRsNABuJ60Zo2H1o8s+tADllx1qXKsOKg8vPelVSvemA4rjPWoXBzU5GR1NIRmgCqaKmZBURXFIBtJSmkNABmkJpM0UAKDS++abS80gJA2KUvUeaN2aAHFqaTSZpKAHUmaTNBNAC5ppOKMmlpgGakh++fpUVSQHMh+lJmtD+Ijyv43jP9g8Z/4+P/AGnXkxOwDqT+tetfG4kHQff7R/7TryUqpPHJrNm9b42MVWZhuIHsKmBVeF5pnQ88U8HA44oMReg96ACThfvd6cIyRksVXqSagmugo8uEfU1SRLkPeSO3HJ3SfyqsFmu5MAEmpraxknbc+QPU9627e3SFQqLVJENlaz0tIQHkwz+nYVpqg4wMU5EJrpdA8JXerOJHUxW/diOv0piObMUrsscQI3dW9Ki1Tw9Db6ZLfM8rTJt5ds9SB0/Gvbk8KWCaZ9kjiAPUSY5z61wXjXTJdP8ADl6sq4I2YI6H51ruyv8A36j/AI4/miZ/Ayp4I0DwpPo0N/q1ve3F45bEYkCwnDEDJ6jgU3xjfeHZXWzi0q2meIEIlrEYlU+7/fc/kKs+EFjbwzZAtz8/H/A2qp4g1LS9Ouv3cQur/GBEgzj/AHsUZo/9urf45fmwgvcRxaaBFFEb2/YW9svTc2c+w9TWbfauJgbawiMFr0Lfxv8AX0HtV+8sdV167829kkU/wReWQEHoBWrZ/C/WLi0a5EqQxgZ3TxlVx7ntXF6jONjAUDBI+lJLIM85LepNal74c1W0aUpGtzBGcGa3O5P8ah0/w9f6hMFEEkaZ5kdSAPzqhGfBbz3twsUCNJIxwABXcafoOn+G7db7WGWW6PKW455qeOSw8MQ/ZtPiFxqLD5pD/D9fT6VThtJ7y68+6Zprhj37fSpbuOwXVxd63OJLn5IFP7uAdB9a6bw/4VudTdSEMcA6sRW74b8EtLtub5dqdQnc16Hb2kVvEscaBVUYAFAGfpej22l24jgQD1bHJrRKgjFSbaMUhnlvxQ/5Fa3/AOv1P/QJK63w2B/wi+kf9eUP/oArlPieP+KShP8A0/R/+i5K63w2v/FLaPkf8uUP/oArfK/+SYw3+Of5s6Mf/vs/l+SJb1SjLIvToajjetCaIPEePwrLAMblT2rmZgXUapAarI1TKaQEmaQmkFFAg6mmyKGXB704cmkPNAGTcQGNiR0qCteWMOpGKzZIihNADFbBpzc9KZilWgY0jmkJ244yTUhFMZNy4pAXLX75+lcrqHGp3I/6aH+ddXaj5z9K5LUWzqlyR/z0IrzKP/Iyqf4UdlT/AHWHqyMGlByaYDW3omjyX0wYr8g616xxGj4Z0xnk891O0dK7QEjgDpUVtbJawrGi4AqWgRIrU4mogeafyaYDwaUGueuPGmgWtzLbzahtlico6+TIcMDgjhfWo/8AhPPDf/QS/wDIEn/xNZ+2p/zL7zF4iivtL70dLkUAiuZ/4Tvw3/0Ev/IEn/xNH/Cd+HM/8hH/AMgSf/E0e2p/zL7w+sUf5196OmzRmuZ/4Tvw5/0Ev/IEn/xNH/Cd+G/+gl/5Ak/+Jo9tT/mX3h9Yo/zr70dLu5o3VzX/AAnfhv8A6CX/AJAk/wDiaQeOvDf/AEEv/IEn/wATR7an/MvvH9Yo/wA6+9HTZyMUbq5oeO/Df/QS/wDIEn/xNL/wnfhr/oJf+QJP/iaPbUv5l94fWKP86+9HRlqYWrnf+E58Nf8AQS/8gSf/ABNH/Cc+G/8AoJf+QJP/AImj21L+ZfeH1ij/ADr70dFvNLvrnB468Nf9BH/yBJ/8TQfHXhr/AKCP/kCT/wCJo9tS/mX3h9Yo/wA6+9HREmmMM1gHx14b/wCgj/5Ak/8AiaT/AITnw3/0Ef8AyBJ/8TR7al/MvvD6zR/nX3o3SKaawz448N441L/yBJ/8TTD438Of9BH/AMgSf/E0e2p/zL7w+s0f5196N7vSVgf8Jt4d/wCgh/5Bk/8AiaT/AITbw9/0EP8AyDJ/8TR7an/MvvD6xR/nX3o38Z55FHIrnz438PjpfE/9sZP/AImrFj4p0fUbuO1tbzzJ5M7U8pxnAJPJGOgNCq027KS+8ar0m7KS+9GzmikFFWahS0UUwCkoNFAB2oooNADCSCRmpbbPmH6VEetS23+sP+7SZrQ/iI8z+NFpNdf2J5S7tvn55A/5515YdMvOAIsY/wBof416n8aLua1/sTyX27vPzwD/AM8/WvMIru/cktNtX/cX/CuaSrOXuWt8z2U8rS/2j2nP15eW3yvrsRDS7wH/AFOf+BD/ABqdNNnUZMe49gCMUyfVpYhtWTc/0FRRahqMhyZ8D/cX/ClbEr+X8QbyR/8AP3/yQSfT9RmOPI2p6b1/xqW30aWMhnQE/UVZjurk9Zc/8BFWo5Zm6t+gp/7T/d/En/hE/wCnv/kg2O0deq/rVhIsHnitjRNAvtZmCxKRH/E5HAr0Wy8BaNFAq3MDTyd2MjL/ACIp/wC0/wB38Q/4RP8Ap7/5IcZ4fh8O27LPqd+GcdIhE5A+vy13UXjHwzCgRL8Ko6AQSf8AxNSDwP4d/wCgf/5Gk/8AiqcPAvhz/oHf+R5P/iqP9p/u/iH/AAif9Pf/ACQfH448MD72o/8AkCT/AOJrnfiH4r8Pap4G1G0srsS3T+VsXyXHSRSeSoHQGui/4QXw1/0Df/I8n/xVY3i3wBp8/hi8j0PS86idnkj7Q399d33mx93PWu3LJ1oY2jKo4qKlG++11cxxCyd0Zey9pzWdr8tr20vboc/4Y1Pw5F8OrW2nnCaoqyZHlyZ5kYjkDHTFUoL7TLWTMQjXnO5EYH65xWLH4B8cwRiOPTNqDoPPgP8A7NTv+EH8dj/mHf8AkeD/AOKr2cdlNaviqlWniaPLKTavN3s22r6bmdCpk6pRVWNXmsr25bX62v07HYnxaqhHXVZWkj/1ZdWbb9Miql34nj1WUNqlw92q/dSQvs/75GBXF3Xh/wAXWTbbizdD9YyPzBqqbDxEP+WR/AxmuX+w8T/0E0P/AAN/5Gvtck/lrfdA6Wa8heTc84kVT8kaxlEX6CquqapcNAILH7zDDTHjaPQA1kRaR4pm/wBXaSt9AlXV8J+NCm82JVfV5IV/maP7CxP/AEE0P/A3/kHtck/lrfdAq2lmkWAzYJOWduc13nh+XwvpirNc34kuP+uMhC/+O1wb6L4oQkGAcekkRH6GlTQ/FUkixrbgs3QeZF/jR/YmJ/6CaH/gb/yHzZL/AC1vugew/wDCcaCrALd/L3Jif/4mpW8ceH+1/wD+QZP/AImvJh4N8cHpYf8AkaH/AOKrMl03xJDM0MkO11OCN0f+NH9h4n/oJof+Bv8AyF7XJP5a33QPah430Dvf/wDkGT/4mlbxt4eOMahj/tjJ/wDE15JD4R8a3EayRWO5GGQfOh/+KqT/AIQrx1/0D/8AyNB/8VR/YeJ/6CaH/gb/AMg9rkn8tb/yQ6T4oTxr4atrct+9e8V1XHVVRwT/AOPD8639B8V6DB4Z0qCa+2zRWcKOvkucEIARkLWL498O6x4hSwTSrX7QsJkMo8xEwTt2/eI9G6V0mkeB9Gj0WwS+03/TFt4xP+/f7+0bujY656cV5mXzf+rWFhTlFy5ptq+qV3uun+QqiwX12bxXPay+G2/z6Wt8yT/hMfD54N//AOQZP/iaz5/E+iMdyXuSP+mT/wCFbLeCPDm3cNO6dvPk/wDiqqSeDtBzlbDA9POf/wCKrkf1n+7+Jt/wif8AT3/yQz18UaMOt5/5Cf8AwqVfFeiD/l9/8hP/AIU9vCGjKf8Ajy4/66v/AI0Dwlon/Pl/5Ff/ABpf7R/d/Ef/AAif9Pf/ACQB4s0T/n+/8hP/AIUf8JZof/P9/wCQn/8AiacPCOif8+P/AJFf/GnDwfoZ/wCXH/yK/wDjT/2j+7+Iv+ET/p7/AOSDB4t0PH/H9/5Cf/4mrVjrmm6nO0NncebIq7yuxhxkDuPcVF/wh+hf8+P/AJGf/wCKrnvC0CW/jLU4Il2xxrKijOcASKBU+0rRlFTtZ9rmqweWV8PVqYZz5oK/vctvwOzIqvLEGHTmrjLUZWuo+eMl4ypplaMsQOeKpum00AR4oA5p2KCVRS7sFUDJJ7UAWLYYb8K4+9H/ABMbr/rs/wD6Ea3dMvze6xJsyIUiIUHvyOTVzTPDTXmoT3VwMQmVio/vc15dH/kZVP8ACjtqf7pD1Zm6JoUt/IHdSIx1Jr0C0tIrOERxqABUkNulvGEjUKo7CpAtescI3cpOM0U8ioyTnAFACEfWlDkfw0HpimUAeZ6Ppdjq/j3WoL+Dzoledwu5lw3mgZ4I9TXWf8IT4bz/AMg3H/beT/4qud8LHHxD1s/9d/8A0atb2qveRanaxxapcJFcO4ZAkR2gKSMZTPbvmuCkqag5SjfV9jzcNTpum5ShfV9F38yb/hB/Df8A0Dv/ACPJ/wDFU3/hCPDn/QO/8jyf/FUyHW2tLi/FwLq5ht0jYuiA7F2fMx6Drk4H5Voz6jFb3spkmcQx2n2hlCArtBPzZ6546dK3jGi1flX3I6FDD/yr7kUf+EI8Of8AQO/8jyf/ABVIfBXhpRltPAHqZ5P/AIqqWq+MFhtmW3hkjm/6aheB68E1wV9rt7euWlnkYH1bj8q56tfDwdlFN+h0U8HRkk3FK/kekL4P8LMcLZofpcv/APFVIPBHhs/8w7/yPJ/8VXkQvZ1bIcj6HFaVn4n1Wy4ivJduCNrHI/Ws44mi/igvwCphMPGPNFJ/JeX+aPSz4I8Ngf8AIN/8jyf/ABVcP4oHh/TpmtrHTV8xeGkaaQ4PsN1ddonjG01CwInLLcQxFnXu+Bkke/Fee+LISdSadI5VinHmRmRdu4HvitK7pcicEtfIdLD4ZTtUil8kYMl4gPyoB+JpYdQjRwZIEcehJGfyNU5Im4wQeccetRNGwxghucfL61z8kLHX7HB/yR+5f5HqPhqLwpriiKTTViuQPu/aJMN9Pmrp/wDhCfDnX+zuP+u8n/xVeMaU8ttOJI3G5T1B6V7nod3cXWnxtcQSxSBRkyAfNx1GCa6sM6c7xlFXXkcVShheb3Ir7vmUx4J8OH/mHf8AkeT/AOKoPgjw7207/wAjSf8AxVS22usRfyXdrLDDbTGMPhTn7oC4DEliT6Y5FXrTU47uaSFoZreeMBjHMADtPQjBII4PeuhQov7K+4xjTw8tor7jLHgnw9307/yPJ/8AFUf8IT4d/wCgd/5Gk/8AiqkbV3tdU1JZIbmeCHy2/dICI12ZJ6jP0GTVmXXLWPeypNLBGqtLNGoKRggEZyc9CDwDgGly0f5V9wlDDdYr7kUD4J8P9tP/API8n/xVH/CFeHv+gd/5Gk/+KrZvb2KxhV5A7l3CRogyzsegA/A/lVF76Z9S01dksCSibzIpAM/KBjOCf0NN06S+yvuKlSoLTkX3Iqf8IT4e/wCgf/5Gk/8Aiq5iysbfTvijHaWkflwR52ruJxmEk8nnqTXb2+rwXE0KLFMsc+RBMygJLgE8c56AnkDIFcbDL53xZEgR0znhxg8QelY1o01yOCXxI5sRCknTcEl7y2R6Diisy2163untwsFykdwxWOV0AUsATt65zwfbjrT7fWILiaJFimWOfIgmZQElwCeOc9ATyBkCupVIvZncqsHszRpKwptZNxPp/wBljuEglugnnMoCSrtbpznqAeQM4rRuJzHqVnCJHXzRJ8gUENgDqeo69qFNPYFVi9i5RWPH4jtpFhYW13tnysTeVw7D+Ec9eDz0461L/bluLVZzDcAtP9nMWzLq/PBAPt2z1o9pHuCrQfU06Q1k/wDCQ24Vy1tdqInEc+Yx+5Jxjcc89QeM8VLdavBbSyqYpnSDHnyooKxZGecnPQg8A4FHtI9w9rDe5dJ5qa1/1h+lc7qmsn7PKtok/wAk6RG4VRsB3qGXrn1HTFa+m3Hmapew+Y7eUE+UqAFyAeCOT+NLnTdka4erF1lFf1v/AJHA/GUIP7Fd8YXz+T/2zryi5l3TQRxjCOqkn1ya9Q+N0ZkOgjPH+kZ/8h15rEIsRM6tvjAAx0OOlKV9LHopU3UlGbS23+V/wIBp43yOSqqHIUEmrMVsSowQM9Ae9TQkDJbcdxyVxkGtTSNKvNWlS3tUckcEj7oHvU3qLRFqGEk+Zu176fNedlpe223XrRtrYy8KMseABXW6L4eSOSWbU7e4MVuoeVYkztUjOWyRxj05rqtJ8EnSvstzB5U11G5MizEqpBBHBAOMZz0ropdJmkTWQrRj7bFsiyTwdhXnj19M0S57u39af5k0ZYfljzpbWfe/Ov8A20igurPTFvQm8w25iVY0jXq4GAp75JHWti2laeLe0EsBzgpLjI/IkfrWamlXEYvvktZhOsSiOXJVgqgMDxx7das6VYzWlnJDOy4eRmSNXZxEpAG0MeSOp7daqLlfUyrQoezvB66fkr/jf/O+8EmsC50+7ltYbhY1gkeK5KgIxAPI5z9MjmpE1cQwWsZhuLq5e3WZ1hUEhSPvHJA5OeOpweKhttP1CHTG0p2tmtlgaBJgW3kYwuVxgYHXk5oi0/UbSSO4tvsrTNax28ySOwXKZwykDJ+8eMDtU3nubuGG1irb6a79rvp36a9h8euvcatb29tbSTWs1v5wlXaOrAZ5YHAycjGc9qg0XXDNaWMd0lwZJyyLcMgCM4ycevQHtjjrT7TR7nT7mxeB4pVjgME3mEqTlgxZcA988frS2+j3EdhpNuWiL2lx5smCcEYcccdfmFJe0vd/1sVP6pyOMbW6Prpz6v8AAm1LUp7PUbG3is5Z1nL7tm3PCk4G5hz3+lPOs2qzsrRz+QkvkvchB5avnGCc568ZxjPepdT0+5aazvYWgDWrszJM5VWVlIPzAHB/CuevpJI7e40y3kgawluDL5pzvAZ95UDoec856dqcpSi2RRp4epCHNa9tdfN3fqla3fzsdVey29nb+a0Ek/ONqAEn88Afia5TVPElo1vbSWNuyuLkJLH5Y3d8qevt0qtqgvb1EeSdXw+5o3cqGXB446ev4Vlx6dMsACLAG+1eeFwQoGMY9qmpObdommEw+HUVKq1e/wDX9WL0viqdzsjRon3BPKAG7JGeucdAe9VJ75mKGZJ5JXzhCATx1744pjWMzvI8kMErSkZQsdqqOmGx15POKQWVzEYHWVHmiVlIcnBBOcZ5ORgdqnmn1NnRwqty2+/rbv2vp+vaNryIeWwV2Mm5QoXnI6g+9JDem31R3ljleKJkY7VB2jGT/nmporB1uopndC26Rn4OCW7D2FSvY3TS3Jg8krOFCsSSR8uMgY5/T60rz3/rYqMcKpOK1TXV/wB7/L7/AMDstTujYaFJqFvH9oOwMmAMYPQ8kcc15/4oiktteZ2tpoRNGJRE+0svJHYkdvWvQ47A6l4QGnwsEf7OIVLdAVAAz+QrL8S+H73Uka/lSBJY7cKY4nLchieCVHGCK1lzN/ccNJ4eFN3SbvLffpb9TC0HxPHbQLBJHLLxuGwDIH4kV1sup20WnC/DM8LBSmwZLbiAuPqSK8kFuybcojMF2kMK6jStZdbP7DJHBJGQFCSj5Men+RRGU2ncK9LCxnFU3dX116f1s7I6hL6Owj3yJK/mSLEojXcSxBxx+FOOqJK8UY+020wuUikidFLfMCRnkjB9Qe1UrzzNlp5OzzPtsW3fnGcN1xVwaPeTXa3k7wCdrmKRkQkqqIGAAJGSfmJ6CvKyaUvqUEvP82duKpUFUc6mjf56dO1r/Owxb25CKPObnVjAen+r5+Wpp9UgW6IWKYw+b5Bn2jy9+cbeuevGcYz3px0e652tCduo/a1yxGVPUHjg8n1qOHQBbXjt9i06aJp2mE0ifvUy27H3TnBPByO1emudHPNYWau392n+Wvb8ihqerf6PKtrHP8kyRNOFGwNvUMvXPqM4xVvUmeFrLym2+ZdIjY7gg8VFcaFqP2aa0t3tjatceerMWDgGTeVxjHXPOfwqa/ZLl7WMSJFNFcpJ5cpK7sZGB+dHvO9yJfV4uCja15fdZWv876dCNNQihhmeRppWF00CIEG4t/dXHUdeT+NOj1iFpfKMFxHJ56wFXUAhiu4d+mKadIuVXzYni8+O9e5jDE7WVgRtPHBwT61Tjs7y6vr9gYBdQXkMwXJ2HEYG3OM9D1x+FJymrGkaGFmpO+y721uktO2u5cvdRVIbwIZYzbSJG7qgbk7TwD7MBzXE2dzJaeJvEEsSSFxHc4ZADsO/hj7Cuzk0i8ltNRWWSDzbqZJF2k7VChBjp/sn6+1cjptvcXfi3X7a2MQaVLiMtITgAyAZ471hX5nKP9dz1cpVCNCsk01ZX/8AJb/jc6vULqW3s9PlUuTJNGrhFyWBByKUapDtYvFNGySpFIjqMoW+6TzjHPUVPe2M8trZpC0Qkt5Ec7ycHaCMcVWbT2eDUJb6SJHuUCnyySsaqDjk4yQSTniul86eh4sVhpU1z9+m/wAS6el393zsiZJbieBVbdDt3NjjJGcfXGPzFZU+pRrMES3nkUymHzFChd46jkg9jT9Pv4bbTFnvJkS4uWMzqOTz046/dArnZdSPmTzw2ysDOxWR3ORk7eI/u7vfrSlN2TKpYWm6s4NX5bLfd7d11Wmu3Q2H1W2QKzrKsbgmNyoxJj05z+YFYF1qz6g6qEdISflX1Pv7/pVnTPDN5cOpS3Qxr/y1xg/yrRTwndJBbRkqXjk3PjOMYPTj3FNOo0r/ANbf8EU4YNOSi79tfKWv4R+/ZdGeFoy6yXLIytgqM4wRn6+1dvDq9tFpk8rRy5tCsbpgbmYhdu3nnO4YrA0zR7nSrNlnEe3eRGysckEk8jHHbuavSW3m6/p8cbAxywpPcp6eV9w/izD/AL4ry6LksfPvyxOqdOhUi19m7at2Wv4q/wA9DavtQjsnhiMUs08xPlwxAFiB1PJAAGR1PcVQ0zU5n0+4uZIriZhdyxpEqDeAGIAPQDA9TVnULO6bULW/szCZYkeJo5iVVlbaeoBwQVHaqH9j6gmn+V5lu8jXr3Ese9kSRWLHaSASOSD36V6snPmOKlHDuik2ru1/venpa3379lvtaMlrZz2yzoftqwTRbMvkZypHI9O/41Fe64QsHlR3MMsd5HHPAyAuQwJA4JBB9QabHod5DahYvsiSLf8A2tUUEIF2429PX/H2pZdHv5pjes9st21xHLsBYoqIrALnGSSWPOB19qh+0OmKwae6sr/ns/K19fJfOe41lHsLpwLi1mt2RZEZFLruIxxkqQc9c+tOu9dgtJbmM291J9lAaZo0BVFIznOfT8eOlV5tHvLm2v3meBbq7aL5VJKIqEEDOMk9ecd6luNMmlXWdrR/6dEEjyTwdhXnj1+tXep/Xz/4BioYRaSf4/4L69l71vT7+G0nUV0/xrrVz5MsyFpl/dbcjMy4PJHHT8639auCk9lcsD+7dh5fVmLKRgdv1rldPX7P4o1d3hjnFv5rsrylB8sg56HPPQHj8q6fUo2uoreeIhJEYSKsnuCCD6cGvM5nytHn5ZQpSoJzWnNLW/azS8vN2I7S6VtF1+ZyV81BEUb7yErtBI9PmB4zxVy+vPsUEmoyp5eLHyFhlHzsQc525+7j3z7Vzc9tOY7gmRVlmZSQpO0AYwM96juIzNnzGZs+poWKcY2S1O+pgsLzJrXXXXpo7L5t6mJO+ZZAoCJjcFU5A+lU2jIDEjGB0rTaycRqpK7s/MfUdP6CoriEqsrHGDjFcN9TvWBwjUaUJXe3zul+Or06abGYU9RSEVdeBjnIA7AConhOT+lNSEsHg+VKbu+uv+Dr6L8PvrxySRPujcqelW9S1q71ZbaO52Yt02IVXHHHX8qrNHg1Np2mTalerbwDMhVm/ADNbQk37q6jqYLDJU5TV1pd36b97/gjNcYAYSdGz8x4qq5YS53tiNsneeBVyaPZujI5DHNQsfmfllDHOR1rRMlYPByspdNPXR6ffbW99dWa3hh44dWTEXm46Iy5BJHH1r1DSL1dOt9RuLmFrSOFowbNG3iLP8Q7YORwOmPWvFYpHguRKrv9c810mneL/s9rdW88IYT+Xl1UKfkOc++a3oz5H/XYwWAw0pOcn1762ul/6Tf7j0Gdbkpcwi3cNd3H2u2TA80MhQ/MpO0LkDksDyBjNTW97KJm1i4tpHBgMey2XcIlVju3FtuWyOgHGO9crceN7PVdSt5306GRYgyCOfDBg2PY4IwOfrXTfZDfW8bwJp62ssHlpAchInJJLKoGGJyOuOldEZqUnyvb+v8AMwp4LCpRlPR9dfN6b9rNfPd2Rcey1CWe9nspLUwXyJtdy2UGzG7AHP04+tMXw4ltOwis7C7hdUGbpPnTaoXj5TkEKDjjnNaljDdWsNtbuYTDFbohK53Fxwfbbj8au10qnF6s8qrh6am7O66FHUbOW5FtLbMiz20vmIH+63ylSDjpwx5qF7O8uLuyuZxbgwmQSIjkjawxwSOTx6CtOircE2J003cw9L0T+znijNnp5WEEJdKmJiMYGRt646nPNcjplvPa/E2GC5kEkyAh3DE7j5J559fTt0HFelVwH/NXv8/88K5a8FHkt/MjjxNOMPZ2/mR0sWjzpp2l27vEWtZ/MkwTgjDjjjr8wpmmaGLBooms9PKRKVW5VMTMMYGfl4OOpzzW4aK6PZR0Z1exgmn2MGDStQjj062d7Y29jKGVwW3uoUqMjGAcH1OfatC4tJJtTsrlSuyASBgTydwAGPyq7SU1TSVio0opW/rQx7fSZ4bXSYmeItZzNJJgnBBVxxx/tCg6Tcby2+PH9ofaup+5txjp1rYpMc0vZx2F7GNrGRc6TPNbatGrxg3kivHkngBEHPH+yfWo7nRc6hcXAs7C6S4YM32lPmjIAU4+U5GAOOOc1uU09KHSi/6/ruDowf8AXr/mc/LpN99mlsontvszXHnq7Ft4HmByuMY655z+FbGmWskOq39wxXZPsKgHkbQAc1LU9r/rT/u0ezS1NsPSiqsWv63/AMzzf4yLuOif9t//AGnXmUcWegr1n4p2NxqFzokFtE0kh8/AUf8AXOl8M+A4LHZc6gBLP1Cfwr/jVo2xH8VnLeG/A91qhWe5BhtuuSOW+leqaXpNrpluIbaFUUe3J+tXo4lUBVAAFTqlMwEUU+l4GBTWyT7UgF47UHJ4zihSccjFL1PqaAGhdvNOqaO3Z+vA/WlmmtrBcyk7uyqMsfw7UXsCTewkVsznLcCq93qltZgpCBJJ04PA+prMv9XubhzEEMUJ6ID8z/U9qpmNWVVddnIHlpwT/wDWqHPsdEKHWQl1cT3rb3l80dAAcKPXApgtIvJ3MmSCAC3+eBVhYdjkqgeVvujbxj+VV5iVb5gCM/OxHIrO50JdEQmNyxHUAnkcKPpVeRiuVUEYIHIB/wD11cWeFYflOWx0JzgevFVZnMhV4j1685wO/wCNMQjEyJhMAg4LqOp9B/jS7d8gzgJyAB1z7VJEoVXfaUGMZDcD2/xNIV8xjErDI++w6gegNAEJWMrsCsEJxluScdhU8SlG29HCkyADAHt/9elcxqkeFCOB8hAAwPU012BBYhipGAGb7x7UBY19Ev8A7Pei1l+WOb7nGPm9P8+1dSQNvQHPt2rgRK37rbkSYy3B+UD8a7PTL5by0DAkuDtY4xk+v41cH0OetC2qPOPGWhf2Zf8A2mFf9HnJPHRW9K5lSRyK9v1PTbfU7GS1nXKOMZ7qfUV45qmm3Gk30lrcKQVPyt2YdiKoxR0FjrsV3MI5IvKz33Z/oK6RNOEihlm4P+z/APXrzKN9jgg9K7jw5rHmxiCRvmHTNeV/YmA/59/i/wDM7HmGJ/m/Bf5GyNJB/wCW/wD45/8AXpf7H/6b/wDjn/16vKeQc1ICe54o/sTAf8+/xf8AmH9oYn+b8F/kZ39j/wDTf/xz/wCvSf2P/wBN/wDxz/69aw56Glxmj+xMB/z7/F/5h/aGJ/m/Bf5GHNpc6LmEpIfRjt/xrKn/ALSgPOmkj1WTP9K6/HOKXHvR/YmA/k/F/wCYf2hif5vwX+Rwxv7lfvWJH/Az/hWda+JnutRms1sdrRbsnzc5wcf3a9LCg8ED8a878Iop+JWuggYH2jqP+my1hVyfBRlFKG77v/M9XL8RUq0MRObu4xuvJ3LqXs79LRifZif6VaiS/mPyWD49S2P6V2AQA4AA47U4JW/9iYD+T8X/AJnk/wBo4n+b8F/kc7BpV3IMyqkQ9N24/wAqsjRCf+Xj/wAc/wDr1t4FHAp/2JgP+ff4v/MP7RxP834L/Ixv7C4/4+f/ABz/AOvTG0bb/wAt/wDxz/69bJYCo2OaP7EwH/Pv8X/mH9o4n+b8F/kY/wDZP/Tb/wAc/wDr1dtraC2yY4o1kYDzHVAC5Axk+tTlxnGKaQPSunDZfhsK3KjGzfm3+bMquLrVVactCQHvTSaYXYdORSeYD9a7DnHUlJkGigANNNONMPNAHmmhwxTePdZEsaPtaZl3KDg+aOR711F1CDnjNc94aTd8QNaHvP8A+jRXYzQ+1eZCnzQb82Y5dNqk15s5meDBOBVGSLrxXQ3EI54rMmg5OBXLOnY9aMjGkj9qqyxBgQRkVrSQmqskWM1hKJtGVtUZTx47VXdK05I6qyJWdi0zNdPat3wZILXXJZzj5bWbGfdcVlulbHhi7g0+5vZJ4hIGtHRQexJFbYd2qK4VJNwscpcDfPIx7sT+tVnStCRMsTjvVd05oU9QRRZKjMdXWjqMpWimMjtJjaTrKEV8HO1uhrstP+IVxbMkf2O3SEH7qg1xxSm7cGtI1HHWLIlCMtz3zSdZtNXtVmtpQ3HzL3U+hrQzXkPga6lg1qNFJ2vwwr10HivUw9V1YXe55tan7OVkKDS0lLW5kFef/wDNXv8AP/PCvQK8/wD+avf5/wCeFc2J+x/iRx4z/l3/AIkd/SUtJXSdgUUUlABRRRQMT/PNIaU01qAG1Na/60/7tQ1Naf60/wC7SexrQ/iIkuYleSNyoLLkA+mcf4UioBUk7bQD/Oo45Ffp/KhDxP8AFZIFAGcZpevSgZbtgVIqFuAOPWmYjAoHJoALHCjJ9qiub6wssC5nTd/cByT+A5qlP4gZSqW1ttBGQ0pA/JalySKjCUtjYWDg7jx7VC+o2VruQP5sg6rHyfxPSueuLue7UedLJKCcBc4UEew4NVkWZ5AkkqxpnJAPI9ahz7G0aHc1rrXLid/KQ/ZgR0HVvxqp5LDLx7gQvzt1P5mmOY/JcoiuyHlgcnH1NI11FtZGYH5eQDwD6ZzUNtm8YqOxYFuJflidwvc96bNcRWqsocF9uGLEHFUnvpQdka7UxtVVHH+Jqm0LM+5svjucYH1/wFFh3NC71VNuyFQOPmk6sfw7VlSXMkgCncqg7umef61OsK46MB2I4/If400Wxxg4A/ujjj3NOwriR3DgsFaM7uSxxgH0PqalXmLar7WOcHb+pqEKkbB1GWHHTAX6UkbZlbcSxA+b1PoKBlqNW2KiKmACFUj82p8GI4yVj3Ioxg/xUxMOfLHyN3PoOnaljlMnmAJ8gIUdBkCgRLGN4Lv36/L0H/66rurRlmBDOWwqMc0+CXchjyrYbJOcCo5vmQMYw7lgVIOPrmkNEgBiRbd1xuwWccr+NX9Nu3srlpWl3IcAoOre/wCFUEAKjc7CJTlj1z/j1p32gvMW2uqDAB+7nv37UJ2E1dWO8SRZY1ZeVPINYfijw6muWXyYW6i+aNvX/ZPsaqaHqzxymC4ceW7bYyOx/wAK6YNnj1FbJ3Rxzi4ux4PIslvK8UqMkiMVZWHII7VZtL1reZXUkYrvfGnhc6hE2oWUebtB86D/AJar/iK8w83BwetIR6xo2qLe2ynI3AVrB+Oa8p0TWDZXKjd8pNekWl4lzCrqQc07iaNAH0NSByKqiSnCWgRYLM3tSiTHXrUIlFMeTkHtQBb3CvPPCBx8StdIx/y8f+jlruRKD3zXA+E3C/EXWj6+f/6NWuav8cPU9zKv91xX+D9T00HA5akLjHWovMppcV0nhE3mU1pKh830pDJTAez0wv71GZKYXPegCXdk9aN1QFsUnmH0zQBPn3qJsbqZvOeaQyUASZI6GjzSDyKgM+O1HmDGaALgcGjrVRJhnGamV896AOD8KjPxE1vP/Tf/ANGrXdugIIrg/Cv/ACUPW/8Atv8A+jVrvmFcuG+B+rOTA/w36szZ4hzxWbNDnPFbU6d6z5UOTWNWB6cJGNLCKpSxda2ZYqqSRZ7VxygbxkYskXtVWSL2rYlh9qqPFWEomqkZLxVGFZN20kZGDWk8XtVdoutRaxdzMeL2qu8dajx+1V3i9qkpMzXSomWrzpUDJVKQyoVphSrJWmla0UgNHw7qI0vVYbhlygOGHtXtFrPHcwJLEwZGGQRXg6LjJr0/wHcO+lvGxJCNxXfgarUvZ9zkxULrmOvopKUV6hwC15//AM1e/wA/88K9A6VwH/NXv8/8+9c2J+x/iRx4z/l3/iR31FKabXSdgUUUGgBKKDRQAhpjU+mNQMbU9p/rT/u1BU9p/rT/ALtJ7GtD+IixMu4DjJpscRzwPxqrqmqR6YkZeJpGfOBnAGMdT+Irm7zWbu8hBZvLibgRr6/Qcn8fyqeZI1rU3KozpLnWLO0LLn7RIo5WPGB9T0FYVxrl7eDbvFvGTxEjYOPdqpqw8kyXMFsATtTevUfQd+vFOS8sJnCvau7d/KYqB9QeKhybHGmo9AdVUE4UNnJKc5qW3IZsqBlR/E2AOvX0FSPYG7jxZSqHB3FJVKyD8OhP+FZ/l/62FstJnbvK4DeoIqTRWNFmGf3kmFG1VCcgfjUcjpFvDqjMGyjbsA+9VVmdGCRJ5bd9vzfp2p/lHZvKuzKcnBy34ntQFyaaSWcZwpLAZUcDP0pvlAFd6gsOgzk/l2qaMoOYzvLfwBske5NR+XumUM5z12oO2KYg8wFH2oD/AHjnAHPc0qkS5JUEjIDDhV9aWd0hVQ2AW/hjHaomO5zvPBHCD734+lAWExuRtk2AOCzd6VldV+dkVAOuc5pvmKECx4HOdoPT8qkhs5Jp1/cM7ddo4H1J7UhlGTfK+AmxD1IXk/SrdsXVFEYAyeAcHA9atPbWlof3kzTyDkxQ/dB/3qsQXnkRtIIIoQAQAq8k+m454x1NAN6aEaWpS3GWWHccM0h25+g6mpoLSwcCGC6dpMH5vL+Vfc1TDzX127nLYHzOTlSPx7Clc7EaG3I8ruf7x/z0pisyWfTZIM3MUqTW+OW6/wD6qpucBXC4eTG3PYeuK0dONyCMLiAcSbjhX9ueKg1Wz+y3JWJlcE7wu7lB3BIoBPWzKoCiXg5RHG7PHPHrTizTDafuj7gzgH8+1REkqIo3JAXlhnP/ANeplkJ+RSpA65/hGc9PWkUIYlaMOJNiLn5g2d3tiun0fVBOxtpfvpwhP8QrnG/1bRIV8tgCSTjHpk+9OjZ4yrRoUXGEKseO/wDhQnZkzipKx3JAZeteceOPCH+s1bTo+eWuIlH/AI8B/P8AOu20vUheQYYgTJwwHf3q86iSPk9q23RxNOLsz54BIOQa63w1rZicQytx71Y8Z+FPscj6jp8X7g/NNEo+4f7w9v5VxsUpjcMDg1JW57LHKJFDKcg1IDiuU8N60J4xFI3zDjmuoByM1RJKGoJplLQIaVAYHJxXD+Fjj4gayf8Arv8A+jVrusVwfhk48fax/wBt/wD0aK5q3xw9T3Mq/wB1xX+D9T0UNkUhJpFOVozXSeGLSdBQTTWNAA7A4wMcVGTStTDQAuaSmn1oByM4pgKeRTTS9aKBDWQNwaYY8DAyKlGaGoApPEVPDEDvVmF+MZNOxRgfjQBxXhU/8XA1n/tv/wCjVr0GvPPCxx4+1j/tt/6NWvQlORXNhfgfqzjwP8N+rGSLmqkkOav7c0xo81pOFzuUrGTJB1qpJDz0raeH2qrJb9T3rlnSNozMSSHjmqckHtW5JADmqz2/Fc0qZrGZhvFVd4eDWzJbH0qu1vWLgaqRjPF7VXeKtiSD2qrLDjtWLiWpGLNHjtVVo/ataWLrVR4xms2jRMz2So9tXnj9KhMdCYyFU4/GvTPA0JTS3cgjc9edqn6V6b4Ouo5dHWNcb4zgiu/L7Orqc2KvyHR4pRSUor2jzgrz/wD5q9/n/nhXoFef/wDNXv8AP/PCubE/Y/xI48Z/y7/xI7+kpTSV1HYFFHaikAlITg44xTqaaACmN9adzimkZoGNqe0/1p/3agxU9p/rT/u0nsa0P4iMrxOiyPZghyfnwF7/AHe/asLzlQbfMAOPkVQT/Tr/AIVteK2ANmNhZ/nIwfTGa5oed5QdY4Y0kXnc/IHSsXuds/iHzMv74xwtKBgl2JU5pySzCJMeVGuDnPykD3qCWd1TG+NmY8BBkdMd+lCys8P7xcuQVDHHX2H+NIRZiMMhEQumbaQ2ORuwOxHT61v2Cx3NsWvYEaRV3rztk2epNZttHHZIEMYaTj90OQvu3/xP51a02QNrUDNOZWbKsR0ORzx7U0RLUlbSre8Qz6dKJFHJhbgg/wBarSu8aHzAAV4KAkbR7j+tX7ALBqeXdFXcVVV98gZqxe239oySxxsIrlCVP+2KCL2dmYcIPmMAfLTAOI26n3qxLJL5cgiibaCMnI4HXrnmkSH7HOAbdjOhG4u2QB/hTp1jEYCMFkAIEfJOMn8hSNAaMCHeu8yHu4BH4CqzQqGY87zyWJq2thd3Ks7naPVvlQfU9zUwmhtRtilWVwuTM2ML/ur/AI0Cv2IIdNSKN3uX8sN82ejN9B2HvUktwZlNvDiNOoVc/gSe9QySNdgqWCg/MTjJb696aMRrwC7gcKXPbuaAt3ARRQQs5OSWwxDck+i56UOzzSNmXjGG2jgD0H9TTDFI4URAvxzsGfwH/wBarHkxqgW9YR55EIbn8ew/GgB0cRlt3hg/1acli2Fx6k/yohaG3cxwgXEqjbvPKjPYDv8AU09kv50EQtcIT+7SP7ig/wARPc0xpUsFMMJDPkmSVRwM9h/U0xD5pDvLmXzJ1Xk8YQ+g9/5VWYhYRK4RpHJIBOeo4B96hBi2eY0heJOckdTinLKJJ2DBlRAeR1Of/wBdIpIjLGIYwd7Yz2OfT/69Nz5CMgBe4cYLf0FTiRYrlZJXDAnhQvJ46e3eh2CTG4cDphVJ4z3/ABoGKFEUKpGsgLHO0dPc4qRJsRBgypKpOc84x7VCEKQu4Q73bP3s4+mPxqWK3M80aPEsaDkZTIJ9fzoAYgljvvtNrLiUAdejAf412NnepeQCReDj5l9K5bYq/Kd3lgdQ2MelRwXD6bcJLCG27cPGTnP49qcXYzqQ50dbdJujA25z29a8n8XeFX0mQ3top+yOfnQf8sif/Zf5V6zb3EdzEsqHIPr1+lRXdpHcROjqDGwIZT0YelXY5djwmyvXtZ1dTjBr0vRNWS+tlBb5gK4jxX4dbQdQ/dZa0lOY2P8ACf7p/p6iqej6o9jcqd3y55oWgPU9bB9807NZ2n3yXkCuhByOavAkdaoQ8tXB+Gv+R81j/tt/6NFd0TXC+Gv+R71j/tt/6NFc1f44ep7eVf7riv8AB+p3sTkrjOKl5qmsp37cd6tjpXSeGIaRsY4NKaaQKAAngU3NKQKTFMApCe1BPpSUAFHakNFAgyRSliabRmgBc00txSmkIoA4bwywXx3q5P8A02/9Giu+SdfWvO9DDHxtquASczdP+ugrs1R/4Qc+9c2F+B+rOPA/w36s2FlU0/cDVKGI4G4nPtVhVYe9dJ2Eh+lROoPapRyKQjiiw7mdNCQcgE1F5eeDitQrmovKAzkCspUkylKxlyQVVkgFbbQKRUT2wPQVzzoGiqHOyw+1UZofaulltfas65t8A8Vy1KTRtGdzmp4jziqjRE9q2ZoDnGKrtbnoBk+lcbg7nQpGSYvaoWi56V0K6JezDKwNipB4Wv3GQij6mn9WqvaLD2sVuzmPLwav6de3GnXAmtn2t0IPRh71ZudEvbYbngYLnGQKpeWyHkEVHLOnK+zKvGaO/wBH8SQagFilAhn/ALpPDfQ1ug5Ga8pj6gg4I9K7HSPEKCARXjkOvAfGQRXrYXGc/u1Nziq0LaxOmrgP+au/5/54V2kepWcn3biM/jXFKwb4thgQQe4/64VtiJJ8ln9pHlYxNezv/Mj0GkpRR2rrOsSkNLRQAlGOKMUppAMIphqQ1GRzQMSp7X/Wn6VBU1r/AK0/7tJ7GtD+IjF8Xj91bNhjw4wue+30rloUkZWDRBCRnL+mMY611fi2SVEtgjqilZCxbtjbiuPgdXO57gsJfmOxTk/55rF7ndP4mPnaSSPd5yIh4YAHn25qe2kitJlU7zckgKGY5jHt2Lc/hViziM9/CoUGJcnGfQZyajsori4uGndkSAsWaUkgdeTnt+tIzuaEEck8qoVKZxsjT7zH1P41radaJa3PmqqDy1Z3cY/Iev171QSdEja3tMqjglriXOX+noP1q3MhstPZGdnluMMQwyFX+FfbP9KYpakFnL5lypEZd2kX5n4+bPNW9RnRNWuJIizSDAIQZI44x/Om6QJxO11dOnk26GRgOgPb8aqtcuZpZYvlBJOSccmkK2psw3dvqCi0ul2SleG+7n0FK1lcWRxa2kb4538ufyJrLMTL80rsZlIyMAdBWxZXUepxMkqyI6fcfOGYevFMlq2q2Mu6a4JDTrPK54AdWwT7DsKpSW90VO+3kLlgMImcD0+lbjC52H7FfNIwz/rmKlf8cVnzpqwBRUnkJXkhsrn86RSZBb6ZeRgSTssKnqzEK2M9DmrJsIGUzNOZIx/yzhGc/U1BHZ6hE4k8pUYn5xLIMY/GpJpLdGDeY7TLgiO36evU9P1oBtsR7h/L8q3Qxq2SEgHzEe5pEgiUbriRtw5WONgxBHq3TPsKZPqFw5SJ2Vc8lEzn8e5qFYgSix4BJG5iOc0DsSzXeYTHbkIjKTJ1yfqfT8qrIqyFg5/cpzxx+QpVCfaWhRUJHUnBz3z15oJWXEIJWP8A5aFjwfxoGtB8g86QRxN5cQwocgZPfpUUW518wgrEgznqG/GoZrjc32e3DmMHaAOPrzVplMkCRouVZh0/i7Z5oGPIMsizMykHO1iM8ZySfTsKdHCVkaW4cEA/KmM8Z6+tQQW++5By8kcYJwO2fSp7grcTMEdjGuQPlweOw9qAEaXz7gKI2VUJ69TT41WWJTvkWIfMM9u/Pv8ASowonTEYC7BnPb3APf8ApS/agGwPmQ84JxjtkUASTugiIjO4HkkcZP0+lVGRXDByMA9ScADH8/SrAAgjDwHZI2WySOOf8MVRJe4l86XMMQPO449s0gRbsNUmsbssqs1szAN7e4/zzXZRypOqujAqwyCD1rhpJhtaJDuVFA3ADkdvxq7o2pvZztBPgwk5Lrkhc/yqou2hjVp3V0dDqOnW+pWMlrcxCSKVcEdPoQexFeK69olz4f1Jrab5oz80UoHDr/j6ivdYyJEGCCPX2rH8R6Jb61p7WkvynO6OQDlG9f8APatGjmTseZ+Gdaa1nWKRvkJ716NFKsqB1OQRXjl1bXGmXz21whjmibBH9R7Gu98K6uLm2ELsN60IbR1JOOK4bw1/yPer/wDbb/0aK7gHiuH8N/8AI9av/wBtv/Rornr/ABw9T2sq/wB1xX+D9TuUjBbNTnNRRnqKlrqPDAv6ioy3PFPNQPGd2VOKAJRzRTfmxSFmGKAHUlG4Um7j0oEKR60lG7PvS0ANoFLSUALSGlptAHDeG/8Ake9X/wC23/o0V3AGDXnIOtaT4l1G8s9Lml82WVQXt3ZSpfORjHoKvf8ACS+Kv+gJ/wCSsv8AjXDRrRpxcZJ7voeZh8RGlFxkne76HfI2DVlCMV5yPE3isH/kCf8AkpL/AI1IPFXi0cDQ/wDyUl/xrb61Ds/uN/r1Ps/uPQ+PT8qYzhea4L/hKvGH/QC/8k5f8aQ+KPF566B/5Jy/40fWodn9wfXqfZ/cd7nd0IqJpCr4IrhP+Ek8W9RoJH0tJv8AGk/4STxduz/Ybf8AgJL/AI0fWodn9wfXqfZ/cd+pDUYrgR4n8Wr00L/yUl/xpf8AhKfFx/5gX/kpL/jR9ah2f3D+vU+z+47p1z1xVC5gBzxXJnxT4t76GP8AwEl/xpjeJPFb9dD/APJSX/GsqleElaz+4qOPpro/uNua2GScUulWivfgsM7RmudbWvFD9dEb8LWX/GnW+t+Kbdy6aExPvaS/41zQcVNNp/cavM6XLaz+49HVQBwKdxXAf8JT4v8A+gD/AOScv+NH/CU+L/8AoA/+Scv+Nd31qHZ/cc/16n2f3HetGrDDAH8KyL3w/aXQJVBG3qK5n/hKfF//AEAf/JSX/GkPifxc3XQf/JSX/GplXpTVpJ/cVHMIR2v9xavPDM1soeI+YD1AHSs/7NNE2GQj8Km/4Sjxd/0Af/JOX/Gon1/xTJ97w6p/7c5f8a4p0qLd43XyNo5tDqn9xZtw24AiqGnEr8TIs+//AKJpx1jxMf8AmXR/4By/40mg22q3Pja31C90ye3Vt29jA6ouIyo5P4d6UI2lFLuuhzYrGQxDhGCekk9j0rtS5o7UV7R2iUUUYoAKSlopAMNRmpGqM9aBiVPa/wCtP+7UFT2v+tP+7SexrQ/iIw/GHH2I4JwXJ7jHy9u9YHltG8Y3tuzxsHPPQA1ueNEVzp+4sMM5yDjgbSaz9LtV8yaRoVLRIWQZzg/5NZPc7KrtJksSJpNo6NGHvJ1JOFysafT1PWsl7u7dmMuArLhWIOEHbArQhja9dsbTKHIYD5mJHv09efalgsrObUYbZpXfqX2H5FHUjPf8PWkZrTcfaxR6Yq3V5+8BAMES/wAfv7AHv3qyt1Lq1woRF3ytkkkjbgd/QYqrcSSzTPI0ZMeSo3Doo6KP5/jV/T9tpps91LvZ5j5cW44Ygjk5pD8+paku7dIUtLcGWEcuygASN689s1VmDFCcrHGSMIDkZ/Lms9vkGFUI/QFshT6AY/nViJVMYSR/OAO0qvCjjOOlA7WLUaJ5bTrEZNzYYnjAzn8eaakqwXKT+aFweCoYgn39f/r1AqzLKsqny0QkbQ2SfXiptispMb+aWBIJJGMdgehoAv6lEby1iu4DiCRdzr90qf6VmJMVV0gZlQHChTn8c/5Fa2l30dqkVvdyqPPZhGzkc4/z+lR3Zns7hvtUMMkbcI2Cpc/UUEJ9DMeORod0LMEXG8kAA++fU0yJZMzeWFjGPvqeTx0HvV95oPIQm0jHVsbmz6etGn3hnuRGbaFIx2KbmYfUmgq7sRRWUXlpLIxhH3t2QzOfUVPNBBDho5P3UikbmHOR1FUpxLeTsiR5WQ5Gcj8PYU25LTosIdWihUiRwSASeoX296AsR+aJ5EjhGE+6e34AU538wBIFU7gM9MDHc/rTEYLJtVAYuxQEjP4fzqV7lYmCRRM77QNvQA9Rj8ulBQoWJWWCIu7v8zkAEMO9OPmy3Jjj8kqP4+hX2579elROr2qSglPMc9AcHtyam2NEVbAY4+8n8XH8qAGSHyl8qIgyHoS3b/IoQyIoRFVpnUjJH+P+eKWVk+z7yyh1wCoHp9KYY/JhaeZyrAZwcAgenHTNAEzSNb4tol3yqo/eDjA7n61HHZNGjSSKMr6EDnPXmp4ZFRZZ5mzIcAKnRccU6WOKTe8kimQx+uf/ANZ5oEQKr7iZBhVCkZGCvqDSXMkbqY1XOCQxJBz78elBijktPKjn4IwSTzweD+HpUMs32RGRkIbKjcPmBFAyGUJEsy+WGYMEBA5zjnOevHeo4Y42URmTbwM5JznGc/SpY4jLOXm3iMORgnB57fzpJ5DAFW2DYK7Wf7wXnj+YpB5G3oOqeWRaTlgpY+WWB456Z/Wujb5xnI9a4SRUEGVeUqgBL9WI/p6Vv6FrIulNrOcSx4w2fvr61pF9Dnqw+0jN8XeGRrdqJYRtvIVOxv7w/un+leZ6deS6bfAkMjI21lPBBHUGvdJASG+ucV57458N71bV7NP3i/8AHwgH3gP4x7jv7VVjFHQ6fdpeWiSqeCK5Lw3/AMj1q/8A22/9Gio/CGr7JPssjcH7tSeGjnxzq5/67f8AowVz1vjh6nt5V/u2K/w/qd3F1NS1FF1NS11HhCE0lKaSgBDSNytLSUAQiTa+09ak4IodA3BFRhWTPcUAIgIcr2qUVGjZY54NSUCFzmkoo5oAKMUEmkyaACkpaOtAxKcGwab2o5oAso9Sg5FUQxFWIpOKYialpBzTqAGkUwqD1qSkpgVmt8PuDN+dPVthwRxU1IVBpWAMj2pNwzTHiJHDEGoj5kfX5h3pgWhRUKTKxx3qbNAC/hSYoFLQAlLSUtABRRRmgAopaMUAJQaKTvSAU02lz60UANPSoz1qUioyKBjantf9af8AdqHFTWv+sP0pPY0ofxEYPjFsNYr5ojzvPTrjbWFp91HbDZdv+5lUo7KcEDt9K2vGw3fYF2FsmTGDjH3ea56BE+zvHNBnGctnNYvc7qnxGn+60yRYrZmeJwCX3ZMgxxkj+Hnp0qzC0lvpc9wwG518mFF+X6nt04qHQo0u7aS03gmJlYccquelW9T8gToJirCNf3UQbCqPdu/TJoMn2KNhFLcuZJpmW1hwWl3bt2f4cevUVflvlnchYU8qIARKy5Ce4x1z+VZkd1DPdK0jPsVcrCFwoHpgcfQ9at+ZIsmQI4QXIG47sZ9QKQ/UkkR5oyyyMGBzhmxjBwf1pIBHHNtEOwAnOOBj1z609VVYxLGWkTB3E8c5zkD04NABSJZpHjQN9/aQCc+3egBLmSGFkVpDK+CM55+tT2NrLeRt52yG2Qku3bbjotLFbpKPOaNRGRl5pRjPsPU1Ne3EcVl9kchPMAwgfGxO2T3JPNAm+iMXUGW5nLW6D5cBAeigHjAxWpZartgSC88y5jkHG7qM88e1Z922VJkYrGhyQhwTx39KWwRZrhzCcIDkgocKO/1OKENpW1Nm900wOZ4GMsf9zqU4/WseR3gZXV9rhQwEbcgnpnPX3rRtLyaMG4hKEPKBIrYAwc4HHtina5ZLaTC8iTPmcFcgDP1psmLs7MryTz3ELF2Cg8FVUDd06461Vjt9x2vIEVRuC+38vwqJGkEjzFw4YghRwcZ61MIXkBXLYAPH8bEnj+tIu1hgmadDHEqoFxl9uCAO+O1OtoxHDmMNIwI3Y6gen+eavLaxLHiS5aIqApWL5sD0J/wpl4klmQlucCRc+YoOB2+v1oC/QrsBCrPOvmSDIxkEkfWlgl8pPOfAXaMqO3U9D/nmoVjYYeKTKdWkUZP1OaLmNrxdnOHy2cDnnt7UDLtsscjmdEjXsoI4BPOajmhkJdGVBDjJ7sfXP17U6IqIvIKqfLGHIJ6c1HJIrhSoKwjaQ+7Bb6+vNAuo6Et8qSgCNhufI+dfToKfNAtwnBCqBlWIwR65I4qCWKY4UgfL9/Y2Ax/DrxT2ukJWFPMBCgbF45IxgGgPQLmdYvlifj7o284UcAj3yKqtavJM1xhWdSCVP071M0TxpLPKUHDBsn24AqGHkNhlEMg5Uc4J/kPfmkMslmuGWNEUEfMygYwD3H61UuoFjjwrSlpE4PQAmrc+IHEhbcwG3C9+Mg1VuSHZZA4G7HEn8A9/TNMSIWnJPlz4VSwyRxu+vvntVdnaG5821fEikcRsCARnP0qe4bzZDFbx/Jxx/D6fiRTIkENttK5cqW3Eds8//q60DOu0jUzf2m+RRHMBiRPf/CrckIlBXGV25PuO4rg4Ly7sZI7u2VpE3fPGecj8K7q0vIry3W4hYmNgMHvWkXc5KkOV3R5j4m0E+HdVW8slIsZH+Uf88m/u/T0/Kl8HS+d4rvpj/wAtIpG/N1NekalZQXtpNbXMYeBxtZfT3Hoa8x8MhNN8XXVsX3BfMhDHjdhxz+lYVl78PU9jKv8AdcV/h/U9Ji4JqSo4uRmpK6jwhOaO1BooAbSUpo+tACUHpR0pCaAIgo8zcD1qT0phIDgY608c0AGcUhpe9GKBCe1J+NLikIoGLSUlANADqSiigBKehptAzTAuRnNPqtGxqwDQIXGO9HeiimAUUUUAGKaQKdSUAMMSdcDNH3fcU6l4NAACCOtLioWUqflpwdifu0ASUCojLg4IIpwcH8aAH0lHajoKAFFLSUo60gCkpaDQA2ilpKAD2pjCn0UAREVNbf6w/SmEVJbjEh+lJ7GtD+Ijn/GUgQWYIU7vMA3Zxn5fT8a5gbk+WSeFBtJB2dfbrXT+MpEUWSNF5m/eBzx/DXMoMysFCpldwCkce39MVi9zuqfEOS5lsw4jkJ80bcr1ZR6elW49jQW+LfLA/wDLXqB7GqTpdGeORsjK/cU84x/Or62/npE0jrG3AIAAUrxxnv3P50EEfkzKFL4jh3KWPTGeoFX/ADFjLfu/tBzwevQDnjr9Kp3bWsL7cs0jEFWByOvapY2mlS38oOFZSBwFAz0OfSkCL/mPOFHmJEsYO5Vzu9/x9KlFlumZ5YrholALOy9R7fpRCEsLPfvV7h1yu0fdXscep5/Kp9Nmn+1CV5n+zqpaTLDC9hn17UEt6aDFVdTvViuJWjjXlYuyoBzz3z6mqt1dCe4ZoV3yhix2D5V/P6DitOySWcySSMqqVYJ/AM4wOvtWZeafdW7L52VBzlomydtAK1ykUDyq2DI7nIQLnaSOAB0/P0q+C3lvBGoQ53TMBwcdlPoKlQJaWfmqh3S7iqg4YqOD+fr25qrNFJcXMTeaACQBEBn6rQF7lxLcTWUccW35sy9OSBxwfXqa2IJlmH2acIS8QdUfnPHese5llGopFEwRIflRgMkn0xVi5uCuuI0ZAVMBzjAUDrk/nTJauMX7Ek+6e02yLxhXIHHse3ep5pbYxPcrDsdSIw+7OM/1/wAafqVmkdybpU8wn5sLzj3x6f1qhFMSzGeJfLlyrx7+cDnOfUe1A1rqU1kkmfERGQMFi2cfT+eKsXR2Q2tq5Xz40G/LbTk8ketSPNHB8llC2/jDSMCF/wBoDHJ9zVRUWK58x2Z3xl5N27POelIvcFtWxIWXEC8bB1/E1HBMsTyR7N7ZAy+QAtWmjlu1fmSAIflJOBx06fjUchWKFsiNjxgM3HvkcA0AMUyiYQDC+YoAJ/u55INTyRmOFUhRcDhSfm4PPSqUaSK/nPlkCkKV4wT6VYSR7a13SMWZ+o4zntigbHTqIU3sQVIAYbjkZ7D1pLVHiAmnkBVQPlIH3fXHWnLtKNcXLZYnKIfmP09qYwSWVDIPLKjo3YZ/xxQIfMou3EsQ/dv95un0NUrlpY2ZVKkhcADGef8ADvUjXZRmVlDb+rL0I9cVI7QiIbZfMJzvKnAx70BsRJJ5m53KhwvznOcD2FZbPJLP5ahUVefmORge/wBM1dg3K4coxdxjnnjPcH2p0rsCWIQoR9wgHv0H4UD2IRCsbl8linK7RyahYh41ZmYRMu7kZxzzj061ZmeElMsLdWYA7V6knAI9vaoxDJPM8MMO5iQE6Z4759KBX6le6uYgkSQjcSCWVQen9Tmui8MWV5ZQSfaWIWUh1i/u8UulaHDat502ZZ+fmb+H6f41rtklc8k8ke1aRj1Zz1Kia5UTSkP3+U9M968iCg+LdSbewlSeRowozubzOn5Zr0nUNXS0YW8CfaL1x8kS9vdvQVxfhSNpfHGpm6VWmUSs2BwH8wZI/M1hX+OHqerlX+64r/B+p3dsCIV3DDEZwamp1NrqPDA02nUnWgBKQ0uabzQAhzSU6mH070ANbhuBmkDkfeGKlA4oZQeCKAEByM0U0DZx2p3WgAzTTTqTFACUUtJQAmKBxS0UwCiikoAkRuasoc4qovWp42oAn6UlLxRTEJSiiigApDS0UANPSlpcUUAIVzSjpRRQBHKgYcdRSBMrmnkZpQu0UARoSDg1LUTgMM8g0IWAw1ICWl6VHupc4FADs0tNFLQAGgUGgUAFFFLQA01JD98/So6kh++fpSexrQ/iIwPFscjmyaKRUdN5yRkn7vSucePzY43aVjL3AAGD2BFdJ4tVyLTZN5eN/H977tcqnkRsvnyfeJzk5waxe53VPiFmHmorSPu5JO7g8Y7jp3q9B9n8pISu5s/M3r7fyqogtlgU7t5J5GM8f1qzp10ULLHbhC3QlMc9+Ow70iBJ45RI6BEGRt54x3/zinaeZXhMbKVAJJAz8wz9etWLuNp9pkIVcgb+Rn/EVBZwmRrmGW4XYACyov3up4/LtQBore28kqxTW8cojXCSCTnrwOPrVqKX7Q0MMoS3tWYEhP4h05PftVG0eNNQRUCNAq7iR15yRU6yTgSFcqjHau3A29MfXg0CaLt2CL95JbmMLndHtIbAHQBf881Qu2n1W63AOqgAJFjJA9Sff+tOtoIQ0uAS+cZLDHv/AJ+tWhA0NrNdyuVLYRAMnAP3sY6UC2IbqZI7kRRFXRVEaAYPTgj86S1ZIIxdSxATLlY1BHLdOAOnv+FVwWhiSQhogxwgY4JbPbPTtVi6JUtEyZ8n5VHXcevA+v8ASmK3Qlije2Mt1cQj922EQD7zk8H1xzmqzSSKdiOrsSeRxt5yQeuPetHUTO/lxBfkjTMpDYO4+/0qmiwld8UQ3bSSR3+vvQNdzSu2cRWk6yJG3lHLZ6dOc1BC8eoxrFclY7kHAmj6P9fSjVIPMtNPZyUUJyOvbNUcK7MCSi9fQ5xQxRWg+eCexlkVkY7jnavO4dsHHFRCIBi7ReZIcFVzt2j6HvWzAPtunCKZiJIVDo4G449vWq8lnb3JBN3JH83SRQA5Pv70WGpdypLLuhQIxYYORngduPTmqiwRqJCW/eqQy7zyD9COKuyxXFg4iVQRnaigYJHqDUMuyONlnkyzKCFDYyfr3pFLyBfOE8b3PG0ZUY4yOpIpkzk3MsoVZIVYBdp6nsOvam28kkm2CKRWbaTIwIOP8alcnADn92DuJZRz7kUD2CVFKpM8hC7thHYex/z3qFW+0OyRPt8zhnZcHA/Hp0oS5E0oXduicfdBAP6fSot3n3aYJWEudy9OmM5P60CK9xEYpgdxZm+7tOAR6EelWoo8AfacGcDJUMNo9vpThFDHN5soIDDIPQnFZjLKZtyhkjHKk5ORSHuWLhZ5D5gVQ+dx5wGPb9P501JIFhSRlB25Pzenfp+WKndlRMlgzgDqMgZHb06Cp9N0S4eRZpmaK3C48nu2fWqSuS5JLUq2trLqWTEm2Nj8zH+H/wCvXS2NhDZW5RQd55Z26mp44UhUrGioF4CgY4pXYQjzGICBcsxOAB1ya0UbHLOo5egjjGXA5LduuKxdQ1hjdGx0oCa7J+Z+qw/X1I9KqXOs3euynT9HzHbDiS7xgsPRfQe9bGmaVb6TbLFCvzH7zHqTTIIdN0pNPRpHYy3MnzSStySa5Xwx/wAj/rP/AG3/APRq13ZA71wnhf8A5H/Wf+2//o1a56/xw9T28q/3XFf4P1O8z1pKM0V0nhgcYppNLTetACUUuKCBQMSkNLijFAhARS8ZphwDSGRR3oAc+CKQdBTBKHPFSCmAUGj+VFABSGiigBO9FLSGgApDS0lACg1Ijc1FilBwaALiNkDjH1qTNV42qYdKYhaX2pKKACgc9aMUYoAKM0uKKAEopcUUAAo5oo9aQDeOnej600xjduGQaXBoACMio2Y5qXBpCtAEHmkZpyXCscZ5psqHBwKgB2jDLyO9AF4EGnA5qgk21uvFXI5A44NAD6KKKACpIfvn6VHUkP3j9KHsa0P4iMLxXHvFphwrZbGRnP3c/wAq5mRITKuYmCkgk4/Sup8UXHkC1G3OSxPGeBt/xrBWSAsux+GOWDNkjg9BWD3O6p8RFG/lriIqSqjt1H93P5UyGWRDKHidGYhiA2B6Eg0kdyqM8ZgkZHwyn39Me9S2xcgtGu4A/OCDxSIsWbORrmDyhIjyRcAscZHTHsf51GkDiZjn5mHynj5WGeMe4pkBlivETzBGN2PMUY7ZIPHSpIoo7iWQvKGCbiexPTn8eaANizt4olBhSJwVBYnnOR+Z5J/Kp5oQI3Y/MA2SSvA/L8BUEYWGRY4U3NjDc/Ko9B7VbkmRUJVwzMgJHpzyfy60yXcpRCUqyJGsZXgSNzuPXitQjGnwSCXaiFizjkDn/IrME/7thuCStwpB4P4du1WrdF8ufTpSdzLvXGeo9KEKSMxS2o30YxujRssdmOB147cfyq9brHbyT38sgy5baM5GSc/oKdbwoLZ5ceTnC785bb7H1OKo3dwZnxJEI4c7UDZyo+nrxk0bBvoNeWWSbdG821xuZ2Xj37ev4U8+XaRRwRL80vV1Iye+DUluTuWOM5jU8MByATyOeBiohJFHIy5ZzyOOWJPqB0ApFGnfgnR7aSXgIQG9j71mtIZQ3lAKiEPuZfb/APXxWzbebHZujsxkfLqrAdB/jVC01NZQ6XYHlcAnYFZTx0x1pkx2JNMQxWF2yOTIu08nGPUZprqL5XltxGrod0qFQfoQfSrESJY3MglcmzuVwrM2RzyMfnUMsE+mXIaEK0bchzzkckgmgL6kmm3X2qE2U7YkChkKnp7VlTINqRxRlVDfNtA65yc1pRKiaxDMiKElCtGCME5OCPfFMu5kWWQRLhVdt5Bx37+9Ipb6FGONImjtrVBt2/vXPUk89e3SmTiZm3DiEDIXB+Yj264/GmSSFsyiZhGuFAYEbh798dqmhZ52MIIEYGwhjkj2+mKBlPZuljRWPzEbz1AOOgqedLaOEjaDIx+6o+c4H6H2qYzLDJIkaAMVCjGMY+tVxDHFGZp3UeYytt6EY/kOOtA7kRga6hDv8ox90nBBzSPHLL+4hZpGcfJzkA9P/r1fhtJ9YBx8sIfO8r95fT8q34LOOy2rCo55Ykck01EynU5TK0bQltIg12fNmwGIPIU+1bLr6Y3elJI24de3fisPxD4rtNDzCB9ovSPkhU9M929BWiskc8pOTuy9f6na6bbPPeSiNAMe7H0A7muU8zUvGM+GDW2mIeI+7+7ep9ulVtM0bUPEl6NR1aRig+6uMAD0UdhXdxQR2sSxRKFRRjimtSdiKzsoNOtlhgUKB196kAJOTTiOaXAxTEMPNcJ4Y/5H7Wf+2/8A6NWu8IwK4Pwx/wAj9rP/AG3/APRq1zV/jh6nuZV/uuK/wfqd2c8YpCaU0xlJ711Hhik0lJkA06kMSijiimAGmFj2FSY9qSgRXZZGb0FCQBQcnNWKTGaAIwgA4GKd0HNLRjNACA0U3aVPWlDZ+tABRS0lABRRRQAlFFFACdqAeaWkC4NAEiMRVhXzVX6U9WxQBcHPSgVEj1KMHFMQuKMUtJQAYoxRRQAUlLSUAFLjikpaQCYopaSgBMUUtFACYqN4wQcipKCM0AVPJQH7tNClG+U4qwykVXk+8KALCyHGDTw4NRxrkZqTZxTAdUkX3j9KrnK1Lbvucj2pPY1ofxEYnisojWcjjJXeQA2Cfu1hh4JgssARSpJG0YNbvikqJrAMwGS+Ae/3a583BWSSNICiHls9B9Kwe53VPiIpbnFyzpHyMbVHA75qG4FxmOaE+W5yPvY3HPpUsryo8TsFXPICdMVNO67fO8xT/C6LznApEDxEbxSGIjduADwNwHIH+NP0uJnm86ULK/mDecfKMcflj0qnbBSJ/tAZSF3REHkY7fWr+kRrLMQwKknkK2eCPvD0oA3Edo0YWyclsZ28j3pHgZdzzMzSJkqOuB2pttdxxlFtf4uAhOc/XFWwkoQlpQGJJAUcE9T1oApRxxbmLqxkJ8ssVzk+9SRrHDei7umwNrANnI9MCnG6+0SZVSjsCVBHGPwqo0UzAByhA598YoE1fcdfXxunSCKFf3eGXn5R/wDXqreW87dz5yg7iDjC54AHerEzxAmCMb2ZPlwuTnoc1DHExg81Y3aQL0fHP1+mKAWgllPMsYjEG0PlZHyBt7cfp61oWdkpngKuzFTkybQBjqM/rVSZo0kViChUYBx1HerulB3l3M527G2Y75H/ANehCltcgmvXe8NzEHZ9wKxheoHTH68mkurdVnUsFS2mJdWbsTzinxQsBAQGVQpCqo+Y59akliM4t8Zx5ZXaB6H3oC9iZFW5sJFAYmFw45z14I9hUUF6lsDbXZd4+SJQfu/Sr1jD5UzwgBoXXBII7+3bmsqazkN28MyBVBIBzw46/gf8aYlZto1bI2b7JPtolj374t64ZOaw7s+ZNOuCIy5YswwxGc9O9SXpNurQIEAUk5Vskj0qqCZT+9QogOAAMZGO5PrjNBSVtSWZsyqASYkTCgDrx1z6etNj33DBY4vIi5VlLckgY4/P+dQsgknjMkaxKvUMTnn/APV+tW4YRdXBjt0CyLkEDp6c9uP60h7DfKaMhWRQycqrJk5Har1joDTkSaiWZRjbHnt159PpWvp+lxWcbMzeZIeWfPfGOKu9+3HerUe5zzrPZEQjVVVAoUAYAAwKidlBO8gAc7jxiq+qavZaLaNPeShAB8o7sfQD1rzDVvEeqeKJzbW6tBZscCNTln/3jVvQxRteIvHBLvYaJh5Puvc4yFP+z6/Wq/hvwm88gvtQ3MWO47zksfU1o+HPCEdoiz3SAv1C+ldeFCqFAxjpQl3C/YYkaQoEQAADoBTW5pzHmmY6VQhBxR0FL9KO1ADTXB+GP+R/1n/tv/6NWu8rg/DH/I/6z/23/wDRq1y1/jh6nuZV/uuK/wAH6nd0GlxRXUeGQkgPzSl1AySKcygjpUBhVWyaAH7sjdmkjcvyBgZpm7bwBxU6YwKAHUEUtGKAG0YooJwM0ABFJQDS0AIelRle9SGkNADM9qBTsU0qexoAWkoyPWl7UAJR1peaKAEopaKAEoFKR6GigBysRU6PVanKcUAWxIpOO9OzzUSAGpCPc0xC0U3BzmnA5FAC005p1Mc7RnFAC0ZNIrblzTqQCUlKRSUALRRRQAlLRRQAGoZYQwqfFIRkUAQI23g1Lnio3TJ6VETIj9MigCwfenW64mOP7tQCTjB61NakmUg/3aHsa0P4iMXxc0arZ+YgJy5DZxj7tYDiBrd85kJUZGeBXQeLEZhaFVU7d5+b/gPFUIHtltV3oBuJ3KuOtYPc7qnxGFuuo3Cxr24yenenwN9tgyTnaSSvTB7VqTt8p2xg7RmNiehrKc4fzWIjyvYdc+1IlaiQCaKRkbaVkGdxORg8Yq3FaWqX0c8MsgVmAkUN93j+Rqtafu5BDJGJA+dgbI25qWS1aK7AiVrdwuWUt/KgGdFGbW3IwoCsRtC8gkdat21zLdxFd6qqH5QRkn8elczZ6naxwpCSI5g5xnJ7da6Gzn326xRIGPRWxgMPpQHQsFljmKQqBhD8w7n2PtVNWujLskZWTGPlOT9T7Vbhgjt4xMwMr9Auex6flSlVjLbAzoEKnHOMAEj3/wDrUAU5IVtf30WxccM4ySc4Bx/ntTZpy8pjkV/mHD+vcge1WzCqpG8s/wA2N3lk9AfYVFK/nAqOU52sowcdhmgVivbyW8e4bedu3cQTu9+fUYqe3kY6iojX5oR989MehPemRWRZ4QzKsYUllb+IepqcyRRKkQYEZIIUZ6dvxHrQJoszW0cMyyxzYWTLsA2OD7VBgABInZzk7c5xnjdk1YuYs2lrOyIzENGwHI9QKo3XMcSQYWSMcgt1Hf8Al/OgUS+iy/aUeEo0g7Dj9OtZ+vXzQ6tPbAcMq568nH6Vn/b5DuuZBmcNmM5wCR+p96p3csktzNJPvmmljG9x0+gApglrcmEsm/ErLmPICLjgmlbzJPNYeaBFkKozhWGPWnQwyKVKIgZjhcHO71AHb8a3dL0BifOvFAB5EOc4+tFrhKSirsq2Wm3WpyAySsLZcbiVxvP+FdTBawW0IjhQKB6Dv71JGoQAKAB6Y4qK7u4bOEzSuFUcnPf2FaKNjlnUch25Qh4G3ua5LxH42tdIZoLUefdHsOg+tYWv+Mp76R7bTgVQnG4dP/r1V0PwlPeyC5uywUnJZupov2Jt3MuKz1XxTqHn3LM5J7/dQV6Bovh+20uMfKGlxyxrTs9PgsYBHCgVRUrLnj+VNIGxSewphJ707BFNORTEN60lO5ptMAFIeaXB70YOaAE+lcF4X/5KBrP/AG3/APRq13xGK4Hwx/yUDWf+2/8A6NWuav8AHD1Pcyr/AHXFf4P1O8pDzS0V0nhiYzSFcindzSUARlAe1KPl+lPowD1FACA0p+lIRiigBDSc06kNACAUUtGKAG0lOIpMUAJRS4ooAaygimD5c5qSkIzQACikpRQAUHrRikbOOKAAmjOaj3EDBpwNADqO9GaKBEqPjirCnIqmDg1Mjc0ATA0oPOKaFy2c1IFoASg8inEYppoAQACnUgFLQAUmKWigBtFOpKAEpaKKACiiigBKTANOpKAGNGrdqfbrtkP0pCKfD98/Sk9jWh/ERg+K41keyDMePMO3OAfu1lWpggZpHQELjBXpWt4tjVo7Z2bGwPj/AMdrHEUP2aABcfxN61hLc76nxDJJWu5HCRHylOMjgn3qErKI2JjXOcKH4JPpWms/mICiqke75RtyahuPNlLpKBlunb5f6UGZnNEZXiG+QsxOcjAz9ash2ZWLRrNtTb8xwx9/wqWWDAi8tGZUfBJPH51JJLDFJ57xoY/ukZ/lQO5QMds4EksLRdRujGefcdat2Ooy28Kw9Yy3ySjnGafK9qqJIwcB1wMdW9Kp2126yNLBb7cjO09H/CgRvw3QuocDeNp4O7kmp7iRLWeN4nXLAYyfu8c/SsCae5ECkoiFuWCHBPFOjkKRxiKKRmBIbdjk49fSiwXNiWSeTmQiNclW2j0HUn6VPvhgthKcEOQF3HJ2/T9a5u4vLtYpEZ4yMjC9h7nHWooNQulmh2RgsOXeU43etFh3Ogl/0i92N8kTLkevToPrUFzdWsTsoCpJtz93OO2BjvWLLJcS3DvJMwViT5cY4B9qjNqhkZnLYPXPVqAubp8QCSKSKVPMikx8oPzAjjiqjG3kx5zXClDnyc9ew+bsPaqsUPy+XFtJwTkKeFNS29jJHKqmPezkHLDdnrggdqBWJGV2MYc7CcptLD5eOg/T61PZWNxfOY7dQCBtcnO3r/P2rbs/DayESXSjaADsXAyfU10cEEUKeWiBAvYDFWo3MZVUtEUrHSIbSMFTvkHVyOc1ohcqW6+opssiQRb3YIoHfvXK6v4qW3Dw23zM3QDr/wDWq9jnbbeprarrdtp0TbnUv9eBXnd/qGoeIboxRlzGT2GMj+gq5aaHe63c/aLgkKT1PQD2rtNP0e206ILEg3d2PU0WuGxgaJ4RhtFWW5AeTsvYV1CxhBgAAe1SEYpjVSQriNyKYaeaQqaBDMU3FPI4o2mgYwrzRtqTFJigCMrmjbUmMUm3NAEJ6V5/4X/5KBrX/bf/ANGrXoZWvPfDH/JQda/7b/8Ao1a5q/xw9T3Mq/3XFf4P1O8opaTFdJ4YYzSd6WkoAKKKKAGmmg/Nigtg0zHzZxQBLRSLTqAEpKdimmgBDxRS9aSgAoxgdaUDmnY4oAjopSKSgBh+9ThSEc0mcEigB1JRnNITQAxwOpoU5FNk3MOOlLGMDFAEgopCcEUZOcUCFp6nmmUoNAFuNvyqYGqkbYNWVPFADjTadSGgBKKKKACiiigAooooAKKKKACiiigBMUUUUAJ1qSL7x+lMp8X3j9KT2NaH8RGD4rhWSO2ZsfLvxk/Sse0XyraOO4cMkvJI5I9q3/EcSTC3R22g7uT+FY0Vkqy4dhhP7xxisJbnfU3JGu7e1gRVRg4J2ZFURcSzs7CEt6EmrtykUKM4YzE8gAZ5qmJrhYAAgBJ+Yn0oMyIi4ZSJGKr/AAqDgVBsUOFmcyqRuU/3fbFTSwmWU+bKFG3t0x6UsKw2p4wzFeCOaBkUkkTOoWCRh6AkinrBdNEGiAVU+bg4p0MrMJduUO454/lTktWf940hcEbigGMgUCYzbJdElsuxyAM9/amtHLsPmJsTj1BBqeJlVWCrsC9GDZIzTESP5gMyKBkZbqaYDM7YUEZbcScg4A+v0pYrTe++4lCoCAy4zVqLdFGpdSMDAUqCWH0qSGJrgS7AXYgldy5I/KgBIDjmNo1TJAweTSeShARYC8jL19ea1bHw68siyuoiHcHkmuks9MtrUgpGA3r1pqLZlKpGOxgWGgXBmWdmNuvZVPOK6C102C25SMBh1bvV4AdcdKgnvIYOWOT3rRRSMJVHInCqIwTwO9UL7VoLJdxdcj1PFc7qnickNFbHc54+XoKzbDRrzVZBLcMxXPLN/SncmwuoapdazKYLcSbCeCCRWppHhZIiJroBm67TW5YaVb2EYCLz3J61dJAFFhXGJGkKhUAAHpQTTSe9MJHWmIcabgUZpKACg0tFADMDNGKdSUAJtoAFO60YxQA0rQFp2M0dKBkTLivOfDH/ACULW/8Atv8A+jVr0kivN/C4z8RNc/7b/wDo1a5q/wAcPU9zKv8AdcV/g/U7zFJTiKCK6TwxtJin4pMUANop1IRQAxlzTcY7VJxQRmgBmaM4oZeDiogSDtYcetAEtFA6UooASkxTqKAAU7HFNp1ADGFNPSptv51GwxQAymsM048UlAEWSh5pV5Oac67hSKuBigAIpjNs5xUuKRlB60AQiTc1TZqB49vNSIxI5oAf3pO9LRQIctWY2yKq1LGw6ZoAt5zRTVORTqAEoxS0lABRS0UAJRS0lABRRiigAooooAKSlooASnxfeP0puKfH978KT2NaH8RGbrkDTJCVGdu7+lYLW4Mm2Q4kJ6E9q2vEGiXes/Z/surz6f5W7d5QJ8zOMZww6YP51gHwFqJfefFF0W/vGNs/+h1yzc+bSN/uPehhKFSPNOsovtaT/JGiIYFRfm4CnP1rNlSeeUPGVBPHPSnDwHqQOR4pugf9xv8A4uk/4QHUP+hnuv8Av23/AMXU81T+T8UV9Qw3/QQv/AZf5FdrZgF85iQx+YCnqbWCfOBKB91R61I3w/v3xu8TXLY6ZjY/+z0i/D29Q5XxLcKevETD/wBno5qn8n4of1DDf9BC/wDAZf5FjyJZmDMyxRngoR0qu75kYRBiyrjB6U5vAOoP97xPdN35jY/+z0DwDqABA8T3WD/0zb/4unzVP5PxQvqGG/6CF/4DL/IrP+7miY/uyR8+3uasRxS3UwWCAszcYHY0n/Cvr4kH/hJbjI6Hym/+LqWPwRq0IxF4svUH+yrj/wBqUc1T+T8UDwOG6V1/4DL/ACOgsNBldD9sI2n+AdfzrZgsLe1iKRRBAO4FcX/wiGuf9DlqP/j/AP8AHKP+EP1w/wDM5aj/AOP/APxyqU5r7H4oxlllCW+IX/gMv8jvYUXoe9JJcxQZ3EEj0rgv+EP1v/ocdQ/8f/8AjlRt4H1Vxh/Fl631Vj/7Up+1qfyfiif7Kw//AEEL/wABl/kdBq/iVLcERMC/TaP61y8TahqspUySPuPTPFKfh3dnr4jnP/bE/wDxdTReBdUgGIfFV5H/ALiMP5SUe1qfyfig/srD/wDQQv8AwGX+R0GmeGYYQHuAHfrjsK6FEWJdqqAK4P8A4Q/Wx/zOOof+P/8Axyj/AIQ7Wz18Y6h/4/8A/HKPaz/k/FB/ZWH/AOghf+Ay/wAjvC4xxURauI/4Q3Wv+hw1D/x//wCOUn/CG61/0OF/+T//AByn7Wp/J+KF/ZOH/wCghf8AgMv8jts0nWuK/wCEN1r/AKHC/wDyf/45R/whutf9Dhf/AJP/APHKPa1P5PxQf2Th/wDoIX/gMv8AI7brRXE/8IbrX/Q4X/5P/wDHKP8AhDda/wChwv8A8n/+OUe1qfyfig/snD/9BC/8Bl/kdtRXE/8ACG61/wBDhqH5P/8AHKP+EN1r/ocL/wDJ/wD45R7Wp/J+KD+ycP8A9BC/8Bl/kdqaTFcX/wAIbrX/AEOF/wDk/wD8co/4Q3Wv+hwv/wAn/wDjlHtan8n4oP7Jw/8A0EL/AMBl/kdtj3orif8AhDda/wChwv8A8n/+OUf8IbrX/Q4ah/4//wDHKPa1P5PxQf2Th/8AoIX/AIDL/I7WiuK/4Q3Wv+hwv/yf/wCOUf8ACG61/wBDhf8A5P8A/HKPa1P5PxQf2Th/+ghf+Ay/yOzNeceFv+Sia5/23/8ARy1qf8IZrP8A0OF/+T//AByrPh/wc+iarPfyam13JNGyNui2kksGLE7jnp+tZyc5zi3G1n5HZQoUMLhq8Y1VJzjZJJrr5o6Aikqbyf8Aa/Sjyf8Aa/Suu6Pn/q1XsRUlTeT/ALX6UeR/tfpRdB9Wq9iCmsM9elWPI/2v0o8j/a/Si6D6tV7FdVA6UuKn8jj736UeR/tfpRdB9Xq9iuQKaybqtfZ/9r9KT7P/ALf6UXQfVqvYpfMhA7U8GrJtc/x/pTTZZH+s/Si6D6tV7ENLUwtMDG/9Kd9m/wBr9KLoPq1XsV6UHNTfZv8Ab/Sj7L/t/pRdB9Wq9hg6VEx+YirXkcfe/SmNabjnf+lF0H1ar2KtJjmrf2T/AG/0pPsf+3+lF0H1ar2KpppOOaufY/8App+lI1lkY3/pRdB9Wq9imGz0p2an/s//AKa/+O//AF6UWB/56/8Ajv8A9ei4fVqvYquAV5pqY6DpVs6eT/y2/wDHf/r0osMD/Wf+O0XQfVqvYq0Crn2L/pp/47R9h/6af+O0XQvq1XsVKcODVn7F/wBNP/HaPsX/AE0/8doug+rVewRtUlIttt/j/SpRHjv+lF0H1ar2I6XFP8v3o2e9F0H1ar2GUU/y/f8ASjy/f9KLoPq1XsMpKf5fv+lHl+/6UXQfVqvYbRTvL/2v0o8s/wB79Kd0H1ar2G0U7y/9r9KDH/tfpSug+rVew2ineWf736UeWf736UXQfVqvYbTk+9SiM/3v0pVXBzmhtGlKhUjNNo//2Q==",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "image = \"Spill.jpg\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "prompt = \"Tell me how to clean my table with an explanation as label. Do not just label the items\"  # @param [\"Show me where my coffee was spilled.\", \"Tell me how to clean my table with an explanation as label. Do not just label the items\",\"Draw a square around the fox' shadow\"] {\"allow-input\":true}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(image)\n",
        "im.thumbnail([640,640], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find bounding boxes\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        system_instruction=bounding_box_system_instructions,\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Generate image with bounding boxes\n",
        "plot_bounding_boxes(im, response.text)\n",
        "im"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A1YTKxQk8imS"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'```json\\n[\\n  {\"box_2d\": [447, 0, 992, 420], \"label\": \"Use this cloth to absorb the spilled liquid by gently dabbing or wiping the area.\"},\\n  {\"box_2d\": [431, 603, 663, 871], \"label\": \"This spill needs to be cleaned immediately to prevent staining the countertop.\"},\\n  {\"box_2d\": [303, 147, 461, 314], \"label\": \"Move these knives away from the spill area to avoid contamination or accidental cuts during cleaning.\"},\\n  {\"box_2d\": [0, 465, 320, 808], \"label\": \"Ensure this sugar container is sealed or moved to prevent it from getting wet or contaminated during cleaning.\"},\\n  {\"box_2d\": [196, 301, 508, 514], \"label\": \"Carefully move this sugar pot away from the spill to protect its contents and prevent it from being knocked over.\"},\\n  {\"box_2d\": [182, 0, 998, 1000], \"label\": \"This white marble-patterned countertop needs to be wiped clean of the spill. After wiping, you may want to use a damp cloth to remove any residue and then dry it thoroughly.\"}\\n]\\n```'"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "response.text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQszSykaTBHo"
      },
      "source": [
        "And if you check the previous examples, the [Japanese food](#scrollTo=tvVSSr7z3uN4) one in particular, multiple other prompt samples are provided to experiment with Gemini reasoning capabilities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQJTJ8wdGOKx"
      },
      "source": [
        "## Experimental: Segmentation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0PDzWh9AGjl-"
      },
      "source": [
        "2.5 models are also able to segment the image and not only draw a bounding box but to also provide a mask of the contour of the items. It's especially useful if you are planning on editing images like in the [Virtual try-on](../examples/Virtual_Try_On.ipynb) example."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "Oz8dSKbeB1J2"
      },
      "outputs": [],
      "source": [
        "# @title Segmentation Utils\n",
        "\n",
        "import dataclasses\n",
        "import numpy as np\n",
        "import base64\n",
        "\n",
        "@dataclasses.dataclass(frozen=True)\n",
        "class SegmentationMask:\n",
        "  # bounding box pixel coordinates (not normalized)\n",
        "  y0: int # in [0..height - 1]\n",
        "  x0: int # in [0..width - 1]\n",
        "  y1: int # in [0..height - 1]\n",
        "  x1: int # in [0..width - 1]\n",
        "  mask: np.array # [img_height, img_width] with values 0..255\n",
        "  label: str\n",
        "\n",
        "def parse_segmentation_masks(\n",
        "    predicted_str: str, *, img_height: int, img_width: int\n",
        ") -> list[SegmentationMask]:\n",
        "  items = json.loads(parse_json(predicted_str))\n",
        "  masks = []\n",
        "  for item in items:\n",
        "    raw_box = item[\"box_2d\"]\n",
        "    abs_y0 = int(item[\"box_2d\"][0] / 1000 * img_height)\n",
        "    abs_x0 = int(item[\"box_2d\"][1] / 1000 * img_width)\n",
        "    abs_y1 = int(item[\"box_2d\"][2] / 1000 * img_height)\n",
        "    abs_x1 = int(item[\"box_2d\"][3] / 1000 * img_width)\n",
        "    if abs_y0 >= abs_y1 or abs_x0 >= abs_x1:\n",
        "      print(\"Invalid bounding box\", item[\"box_2d\"])\n",
        "      continue\n",
        "    label = item[\"label\"]\n",
        "    png_str = item[\"mask\"]\n",
        "    if not png_str.startswith(\"data:image/png;base64,\"):\n",
        "      print(\"Invalid mask\")\n",
        "      continue\n",
        "    png_str = png_str.removeprefix(\"data:image/png;base64,\")\n",
        "    png_str = base64.b64decode(png_str)\n",
        "    mask = Image.open(io.BytesIO(png_str))\n",
        "    bbox_height = abs_y1 - abs_y0\n",
        "    bbox_width = abs_x1 - abs_x0\n",
        "    if bbox_height < 1 or bbox_width < 1:\n",
        "      print(\"Invalid bounding box\")\n",
        "      continue\n",
        "    mask = mask.resize((bbox_width, bbox_height), resample=Image.Resampling.BILINEAR)\n",
        "    np_mask = np.zeros((img_height, img_width), dtype=np.uint8)\n",
        "    np_mask[abs_y0:abs_y1, abs_x0:abs_x1] = mask\n",
        "    masks.append(SegmentationMask(abs_y0, abs_x0, abs_y1, abs_x1, np_mask, label))\n",
        "  return masks\n",
        "\n",
        "def overlay_mask_on_img(\n",
        "    img: Image,\n",
        "    mask: np.ndarray,\n",
        "    color: str,\n",
        "    alpha: float = 0.7\n",
        ") -> Image.Image:\n",
        "    \"\"\"\n",
        "    Overlays a single mask onto a PIL Image using a named color.\n",
        "\n",
        "    The mask image defines the area to be colored. Non-zero pixels in the\n",
        "    mask image are considered part of the area to overlay.\n",
        "\n",
        "    Args:\n",
        "        img: The base PIL Image object.\n",
        "        mask: A PIL Image object representing the mask.\n",
        "              Should have the same height and width as the img.\n",
        "              Modes '1' (binary) or 'L' (grayscale) are typical, where\n",
        "              non-zero pixels indicate the masked area.\n",
        "        color: A standard color name string (e.g., 'red', 'blue', 'yellow').\n",
        "        alpha: The alpha transparency level for the overlay (0.0 fully\n",
        "               transparent, 1.0 fully opaque). Default is 0.7 (70%).\n",
        "\n",
        "    Returns:\n",
        "        A new PIL Image object (in RGBA mode) with the mask overlaid.\n",
        "\n",
        "    Raises:\n",
        "        ValueError: If color name is invalid, mask dimensions mismatch img\n",
        "                    dimensions, or alpha is outside the 0.0-1.0 range.\n",
        "    \"\"\"\n",
        "    if not (0.0 <= alpha <= 1.0):\n",
        "        raise ValueError(\"Alpha must be between 0.0 and 1.0\")\n",
        "\n",
        "    # Convert the color name string to an RGB tuple\n",
        "    try:\n",
        "        color_rgb: Tuple[int, int, int] = ImageColor.getrgb(color)\n",
        "    except ValueError as e:\n",
        "        # Re-raise with a more informative message if color name is invalid\n",
        "        raise ValueError(f\"Invalid color name '{color}'. Supported names are typically HTML/CSS color names. Error: {e}\")\n",
        "\n",
        "    # Prepare the base image for alpha compositing\n",
        "    img_rgba = img.convert(\"RGBA\")\n",
        "    width, height = img_rgba.size\n",
        "\n",
        "    # Create the colored overlay layer\n",
        "    # Calculate the RGBA tuple for the overlay color\n",
        "    alpha_int = int(alpha * 255)\n",
        "    overlay_color_rgba = color_rgb + (alpha_int,)\n",
        "\n",
        "    # Create an RGBA layer (all zeros = transparent black)\n",
        "    colored_mask_layer_np = np.zeros((height, width, 4), dtype=np.uint8)\n",
        "\n",
        "    # Mask has values between 0 and 255, threshold at 127 to get binary mask.\n",
        "    mask_np_logical = mask > 127\n",
        "\n",
        "    # Apply the overlay color RGBA tuple where the mask is True\n",
        "    colored_mask_layer_np[mask_np_logical] = overlay_color_rgba\n",
        "\n",
        "    # Convert the NumPy layer back to a PIL Image\n",
        "    colored_mask_layer_pil = Image.fromarray(colored_mask_layer_np, 'RGBA')\n",
        "\n",
        "    # Composite the colored mask layer onto the base image\n",
        "    result_img = Image.alpha_composite(img_rgba, colored_mask_layer_pil)\n",
        "\n",
        "    return result_img\n",
        "\n",
        "def plot_segmentation_masks(img: Image, segmentation_masks: list[SegmentationMask]):\n",
        "    \"\"\"\n",
        "    Plots bounding boxes on an image with markers for each a name, using PIL, normalized coordinates, and different colors.\n",
        "\n",
        "    Args:\n",
        "        img: The PIL.Image.\n",
        "        segmentation_masks: A string encoding as JSON a list of segmentation masks containing the name of the object,\n",
        "         their positions in normalized [y1 x1 y2 x2] format, and the png encoded segmentation mask.\n",
        "    \"\"\"\n",
        "    # Define a list of colors\n",
        "    colors = [\n",
        "    'red',\n",
        "    'green',\n",
        "    'blue',\n",
        "    'yellow',\n",
        "    'orange',\n",
        "    'pink',\n",
        "    'purple',\n",
        "    'brown',\n",
        "    'gray',\n",
        "    'beige',\n",
        "    'turquoise',\n",
        "    'cyan',\n",
        "    'magenta',\n",
        "    'lime',\n",
        "    'navy',\n",
        "    'maroon',\n",
        "    'teal',\n",
        "    'olive',\n",
        "    'coral',\n",
        "    'lavender',\n",
        "    'violet',\n",
        "    'gold',\n",
        "    'silver',\n",
        "    ] + additional_colors\n",
        "    font = ImageFont.truetype(\"NotoSansCJK-Regular.ttc\", size=14)\n",
        "\n",
        "    # Do this in 3 passes to make sure the boxes and text are always visible.\n",
        "\n",
        "    # Overlay the mask\n",
        "    for i, mask in enumerate(segmentation_masks):\n",
        "      color = colors[i % len(colors)]\n",
        "      img = overlay_mask_on_img(img, mask.mask, color)\n",
        "\n",
        "    # Create a drawing object\n",
        "    draw = ImageDraw.Draw(img)\n",
        "\n",
        "    # Draw the bounding boxes\n",
        "    for i, mask in enumerate(segmentation_masks):\n",
        "      color = colors[i % len(colors)]\n",
        "      draw.rectangle(\n",
        "          ((mask.x0, mask.y0), (mask.x1, mask.y1)), outline=color, width=4\n",
        "      )\n",
        "\n",
        "    # Draw the text labels\n",
        "    for i, mask in enumerate(segmentation_masks):\n",
        "      color = colors[i % len(colors)]\n",
        "      if mask.label != \"\":\n",
        "        draw.text((mask.x0 + 8, mask.y0 - 20), mask.label, fill=color, font=font)\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8t-MOFZcGjl_"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "```json\n",
            "[\n",
            "  {\"box_2d\": [168, 97, 360, 228], \"mask\": \"\", \"label\": \"glass jar with sprinkles\"},\n",
            "  {\"box_2d\": [276, 804, 467, 936], \"mask\": \"\", \"label\": \"glass jar with sprinkles\"},\n",
            "  {\"box_2d\": [260, 699, 431, 810], \"mask\": \"\", \"label\": \"glass jar with milk\"},\n",
            "  {\"box_2d\": [272, 296, 376, 396], \"mask\": \"\", \"label\": \"glass body of red jar\"},\n",
            "  {\"box_2d\": [272, 296, 376, 396], \"mask\": \"\", \"label\": \"metal lid of red jar\"},\n",
            "  {\"box_2d\": [267, 617, 368, 706], \"mask\": \"\", \"label\": \"small metal bowl\"},\n",
            "  {\"box_2d\": [147, 391, 362, 584], \"mask\": \"\", \"label\": \"large metal pot\"},\n",
            "  {\"box_2d\": [190, 252, 340, 381], \"mask\": \"\", \"label\": \"small metal container\"},\n",
            "  {\"box_2d\": [203, 552, 278, 896], \"mask\": \"\", \"label\": \"wooden rolling pin\"},\n",
            "  {\"box_2d\": [57, 233, 220, 332], \"mask\": \"\", \"label\": \"wooden utensils in small container\"},\n",
            "  {\"box_2d\": [0, 369, 167, 586], \"mask\": \"\", \"label\": \"wooden utensils in large pot\"},\n",
            "  {\"box_2d\": [508, 798, 691, 960], \"mask\": \"\", \"label\": \"metal lid of sprinkle jar\"},\n",
            "  {\"box_2d\": [260, 699, 431, 810], \"mask\": \"\", \"label\": \"metal lid of milk jar\"},\n",
            "  {\"box_2d\": [706, 654, 900, 1000], \"mask\": \"\", \"label\": \"wooden spoon with frosting\"},\n",
            "  {\"box_2d\": [680, 756, 812, 1000], \"mask\": \"\", \"label\": \"wooden spoon\"}\n",
            "]\n",
            "```\n"
          ]
        }
      ],
      "source": [
        "image = \"Cupcakes.jpg\" # @param [\"Socks.jpg\",\"Vegetables.jpg\",\"Japanese_bento.png\",\"Cupcakes.jpg\",\"Origamis.jpg\",\"Fruits.jpg\",\"Cat.jpg\",\"Pumpkins.jpg\",\"Breakfast.jpg\",\"Bookshelf.jpg\", \"Spill.jpg\"] {\"allow-input\":true}\n",
        "prompt = \"Give the segmentation masks for the metal, wooden and glass small items (ignore the table). Output a JSON list of segmentation masks where each entry contains the 2D bounding box in the key \\\"box_2d\\\", the segmentation mask in key \\\"mask\\\", and the text label in the key \\\"label\\\". Use descriptive labels.\"  # @param {type:\"string\"}\n",
        "\n",
        "# Load and resize image\n",
        "im = Image.open(BytesIO(open(image, \"rb\").read()))\n",
        "im.thumbnail([1024,1024], Image.Resampling.LANCZOS)\n",
        "\n",
        "# Run model to find segmentation masks\n",
        "response = client.models.generate_content(\n",
        "    model=model_name,\n",
        "    contents=[prompt, im],\n",
        "    config = types.GenerateContentConfig(\n",
        "        temperature=0.5,\n",
        "        safety_settings=safety_settings,\n",
        "        thinking_config=types.ThinkingConfig(\n",
        "          thinking_budget=0\n",
        "        )\n",
        "    )\n",
        ")\n",
        "\n",
        "# Check output\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HxHD1miyLWhK"
      },
      "source": [
        "The model predicts a JSON list, where each item represents a segmentation mask. Each item has a bounding box (\"`box_2d`\") in the format `[y0, x0, y1, x1]` with normalized coordinates between 0 and 1000, a label (\"`label`\") that identifies the object, and lastly the segmentation mask inside the bounding box, as base64 encoded png.\n",
        "\n",
        "To use the mask, first you need to do base64 decoding, and then loading this string as a png. This will give you a probability map with values between 0 and 255. The mask needs to be resized to match the bounding box dimensions, then you can apply your confidence threshold, e.g. binarizing at 127 for the midpoint. Finally, pad the mask into an array of the size of the full image.\n",
        "\n",
        "All these steps are done by the the `parse_segmentation_masks` function provided earlier.\n",
        "\n",
        "Ultimately, use the `plot_segmentation_masks` function to visualize the decoded masks by overlaying it on the image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NlHJHesPUJCs"
      },
      "outputs": [],
      "source": [
        "segmentation_masks = parse_segmentation_masks(response.text, img_height=im.size[1], img_width=im.size[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hf6T89h34mXx"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Output hidden; open in https://colab.research.google.com to view."
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_segmentation_masks(im, segmentation_masks)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ht_OJ5-Q9vfN"
      },
      "source": [
        "## Preliminary capabilities: pointing and 3D boxes\n",
        "\n",
        "Pointing and 3D bounding boxes are experimental model capabilities. Check this [other notebook](../examples/Spatial_understanding_3d.ipynb) to get a sneak peek on those upcoming capabilities.\n",
        "\n",
        "<a href=\"../examples/Spatial_understanding_3d.ipynb\"><img src=\"https://storage.googleapis.com/generativeai-downloads/images/box_3d.png\" height=\"400\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "245bc92a470f"
      },
      "source": [
        "## What's next?\n",
        "\n",
        "For a more end-to-end example, the code from the [AI Studio Spatial understanding example](https://aistudio.google.com/starter-apps/spatial)  is available on [Github](https://github.com/google-gemini/starter-applets/tree/main/spatial).\n",
        "\n",
        "You'll also find multiple other examples of Gemini apabilities in the [quickstart folder](https://github.com/google-gemini/cookbook/tree/main/quickstarts/), in particular the [Live API](./Get_started_LiveAPI.ipynb) and the [video understanding](./Video_understanding.ipynb) one.\n",
        "\n",
        "Related to image recognition and reasoning, [Market a jet backpack](../examples/Market_a_Jet_Backpack.ipynb) and [Guess the shape](../examples/Guess_the_shape.ipynb) examples are worth checking to continue your Gemini API discovery (Note: these examples still use the old SDK). And of course the [pointing and 3d boxes](../examples/Spatial_understanding_3d.ipynb) example referenced earlier."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "Spatial_understanding.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
